load(file = "data/manipulationDatasets.RData")

dat.1

• base R syntax provides ways to manipulate and access a variety of data structures
• code can be difficult to read and keep organised
• exacerbated for grouped data

• enter the tidyverse ecosystem
• a coherent set of manipulation focussed packages

Piping

• %>%

data %>%
    select(...) %>%
    group_by(...) %>%
    summarise(...)

Any object can be passed (piped) to any function if that object is normally the function’s first argument

load(file = "../data/manipulationDatasets.RData")

dat.1

• re(arranging) data
• subsetting data
• summarising/aggregating data
• joining datasets

library(dplyr)
library(tidyr)
#OR to get the entire ecosystem
library(tidyverse)

head(dat.1)

##   Treatment Plot Dose Time Resp1 Resp2
## 1   Control   P1    H    1  8.12  3.06
## 2   Control   P1    H    2 20.55 25.94
## 3   Control   P1    H    3 27.49 29.85
## 4   Control   P1    H    4 44.79 25.39
## 5   Control   P1    M    1 20.99 20.31
## 6   Control   P1    M    2 37.54 17.62

#OR
dat.1 %>% head()

##   Treatment Plot Dose Time Resp1 Resp2
## 1   Control   P1    H    1  8.12  3.06
## 2   Control   P1    H    2 20.55 25.94
## 3   Control   P1    H    3 27.49 29.85
## 4   Control   P1    H    4 44.79 25.39
## 5   Control   P1    M    1 20.99 20.31
## 6   Control   P1    M    2 37.54 17.62

#OR
dat.1 %>% head

##   Treatment Plot Dose Time Resp1 Resp2
## 1   Control   P1    H    1  8.12  3.06
## 2   Control   P1    H    2 20.55 25.94
## 3   Control   P1    H    3 27.49 29.85
## 4   Control   P1    H    4 44.79 25.39
## 5   Control   P1    M    1 20.99 20.31
## 6   Control   P1    M    2 37.54 17.62

str(dat.1)

## 'data.frame':    48 obs. of  6 variables:
##  $ Treatment: Factor w/ 2 levels "Control","Exclusion": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Plot     : Factor w/ 4 levels "P1","P2","P3",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Dose     : Factor w/ 3 levels "H","L","M": 1 1 1 1 3 3 3 3 2 2 ...
##  $ Time     : int  1 2 3 4 1 2 3 4 1 2 ...
##  $ Resp1    : num  8.12 20.55 27.49 44.79 20.99 ...
##  $ Resp2    : num  3.06 25.94 29.85 25.39 20.31 ...

glimpse(dat.1)

## Rows: 48
## Columns: 6
## $ Treatment <fct> Control, Control, Control, Control, Control, Control, Control, Control…
## $ Plot      <fct> P1, P1, P1, P1, P1, P1, P1, P1, P1, P1, P1, P1, P2, P2, P2, P2, P2, P2…
## $ Dose      <fct> H, H, H, H, M, M, M, M, L, L, L, L, H, H, H, H, M, M, M, M, L, L, L, L…
## $ Time      <int> 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4…
## $ Resp1     <dbl> 8.12, 20.55, 27.49, 44.79, 20.99, 37.54, 61.46, 82.21, 31.73, 59.08, 9…
## $ Resp2     <dbl> 3.06, 25.94, 29.85, 25.39, 20.31, 17.62, 98.44, 160.01, 21.22, 37.51, …

dat.1 %>% as_tibble

## # A tibble: 48 x 6
##    Treatment Plot  Dose   Time Resp1  Resp2
##    <fct>     <fct> <fct> <int> <dbl>  <dbl>
##  1 Control   P1    H         1  8.12   3.06
##  2 Control   P1    H         2 20.6   25.9 
##  3 Control   P1    H         3 27.5   29.8 
##  4 Control   P1    H         4 44.8   25.4 
##  5 Control   P1    M         1 21.0   20.3 
##  6 Control   P1    M         2 37.5   17.6 
##  7 Control   P1    M         3 61.5   98.4 
##  8 Control   P1    M         4 82.2  160.  
##  9 Control   P1    L         1 31.7   21.2 
## 10 Control   P1    L         2 59.1   37.5 
## # … with 38 more rows

dat.1 %>% head(2)

##   Treatment Plot Dose Time Resp1 Resp2
## 1   Control   P1    H    1  8.12  3.06
## 2   Control   P1    H    2 20.55 25.94

Sorting by Resp1

dat.1 %>% arrange(Resp1)

##    Treatment Plot Dose Time  Resp1  Resp2
## 1    Control   P1    H    1   8.12   3.06
## 2    Control   P2    H    1   8.14  23.93
## 3    Control   P2    H    2  13.36  28.02
## 4  Exclusion   P4    H    1  18.82  28.60
## 5    Control   P2    M    1  19.95  19.73
## 6    Control   P1    H    2  20.55  25.94
## 7    Control   P1    M    1  20.99  20.31
## 8  Exclusion   P3    H    1  21.86  23.58
## 9    Control   P1    H    3  27.49  29.85
## 10   Control   P1    L    1  31.73  21.22
## 11   Control   P2    L    1  32.76  30.70
## 12   Control   P2    H    3  33.37  37.17
## 13   Control   P1    M    2  37.54  17.62
## 14 Exclusion   P3    M    1  38.57  30.63
## 15 Exclusion   P4    M    1  39.51  45.90
## 16 Exclusion   P4    H    2  39.82  39.07
## 17 Exclusion   P3    H    2  39.83  28.03
## 18   Control   P2    H    4  39.87  38.25
## 19   Control   P2    M    2  42.83  40.52
## 20   Control   P1    H    4  44.79  25.39
## 21 Exclusion   P4    L    1  57.93  85.24
## 22   Control   P1    L    2  59.08  37.51
## 23 Exclusion   P3    H    3  59.53  21.32
## 24 Exclusion   P3    L    1  61.16  39.53
## 25   Control   P1    M    3  61.46  98.44
## 26   Control   P2    L    2  62.35 123.78
## 27   Control   P2    M    3  62.46   4.81
## 28 Exclusion   P4    H    3  63.30  93.43
## 29 Exclusion   P3    H    4  75.59  90.76
## 30 Exclusion   P4    M    2  79.24  88.04
## 31 Exclusion   P3    M    2  81.25  83.61
## 32   Control   P2    M    4  81.78 136.66
## 33   Control   P1    M    4  82.21 160.01
## 34 Exclusion   P4    H    4  82.29  60.15
## 35   Control   P2    L    3  90.22 113.87
## 36   Control   P1    L    3  94.54 119.22
## 37   Control   P2    L    4 114.03  76.52
## 38 Exclusion   P4    L    2 117.88 167.90
## 39 Exclusion   P3    L    2 119.84 110.27
## 40   Control   P1    L    4 121.17 116.45
## 41 Exclusion   P4    M    3 122.09  84.19
## 42 Exclusion   P3    M    3 124.08 124.09
## 43 Exclusion   P3    M    4 159.69 112.65
## 44 Exclusion   P4    M    4 161.67 256.34
## 45 Exclusion   P3    L    3 175.87 286.33
## 46 Exclusion   P4    L    3 181.09 314.49
## 47 Exclusion   P3    L    4 238.76  54.23
## 48 Exclusion   P4    L    4 242.31 304.70

dat.1 %>% head(2)

##   Treatment Plot Dose Time Resp1 Resp2
## 1   Control   P1    H    1  8.12  3.06
## 2   Control   P1    H    2 20.55 25.94

Sorting by Resp1 (descending order)

dat.1 %>% arrange(-Resp1)

##    Treatment Plot Dose Time  Resp1  Resp2
## 1  Exclusion   P4    L    4 242.31 304.70
## 2  Exclusion   P3    L    4 238.76  54.23
## 3  Exclusion   P4    L    3 181.09 314.49
## 4  Exclusion   P3    L    3 175.87 286.33
## 5  Exclusion   P4    M    4 161.67 256.34
## 6  Exclusion   P3    M    4 159.69 112.65
## 7  Exclusion   P3    M    3 124.08 124.09
## 8  Exclusion   P4    M    3 122.09  84.19
## 9    Control   P1    L    4 121.17 116.45
## 10 Exclusion   P3    L    2 119.84 110.27
## 11 Exclusion   P4    L    2 117.88 167.90
## 12   Control   P2    L    4 114.03  76.52
## 13   Control   P1    L    3  94.54 119.22
## 14   Control   P2    L    3  90.22 113.87
## 15 Exclusion   P4    H    4  82.29  60.15
## 16   Control   P1    M    4  82.21 160.01
## 17   Control   P2    M    4  81.78 136.66
## 18 Exclusion   P3    M    2  81.25  83.61
## 19 Exclusion   P4    M    2  79.24  88.04
## 20 Exclusion   P3    H    4  75.59  90.76
## 21 Exclusion   P4    H    3  63.30  93.43
## 22   Control   P2    M    3  62.46   4.81
## 23   Control   P2    L    2  62.35 123.78
## 24   Control   P1    M    3  61.46  98.44
## 25 Exclusion   P3    L    1  61.16  39.53
## 26 Exclusion   P3    H    3  59.53  21.32
## 27   Control   P1    L    2  59.08  37.51
## 28 Exclusion   P4    L    1  57.93  85.24
## 29   Control   P1    H    4  44.79  25.39
## 30   Control   P2    M    2  42.83  40.52
## 31   Control   P2    H    4  39.87  38.25
## 32 Exclusion   P3    H    2  39.83  28.03
## 33 Exclusion   P4    H    2  39.82  39.07
## 34 Exclusion   P4    M    1  39.51  45.90
## 35 Exclusion   P3    M    1  38.57  30.63
## 36   Control   P1    M    2  37.54  17.62
## 37   Control   P2    H    3  33.37  37.17
## 38   Control   P2    L    1  32.76  30.70
## 39   Control   P1    L    1  31.73  21.22
## 40   Control   P1    H    3  27.49  29.85
## 41 Exclusion   P3    H    1  21.86  23.58
## 42   Control   P1    M    1  20.99  20.31
## 43   Control   P1    H    2  20.55  25.94
## 44   Control   P2    M    1  19.95  19.73
## 45 Exclusion   P4    H    1  18.82  28.60
## 46   Control   P2    H    2  13.36  28.02
## 47   Control   P2    H    1   8.14  23.93
## 48   Control   P1    H    1   8.12   3.06

dat.1 %>% head(2)

##   Treatment Plot Dose Time Resp1 Resp2
## 1   Control   P1    H    1  8.12  3.06
## 2   Control   P1    H    2 20.55 25.94

Sorting by Dose and then Resp1

dat.1 %>% arrange(Dose,Resp1)

##    Treatment Plot Dose Time  Resp1  Resp2
## 1    Control   P1    H    1   8.12   3.06
## 2    Control   P2    H    1   8.14  23.93
## 3    Control   P2    H    2  13.36  28.02
## 4  Exclusion   P4    H    1  18.82  28.60
## 5    Control   P1    H    2  20.55  25.94
## 6  Exclusion   P3    H    1  21.86  23.58
## 7    Control   P1    H    3  27.49  29.85
## 8    Control   P2    H    3  33.37  37.17
## 9  Exclusion   P4    H    2  39.82  39.07
## 10 Exclusion   P3    H    2  39.83  28.03
## 11   Control   P2    H    4  39.87  38.25
## 12   Control   P1    H    4  44.79  25.39
## 13 Exclusion   P3    H    3  59.53  21.32
## 14 Exclusion   P4    H    3  63.30  93.43
## 15 Exclusion   P3    H    4  75.59  90.76
## 16 Exclusion   P4    H    4  82.29  60.15
## 17   Control   P1    L    1  31.73  21.22
## 18   Control   P2    L    1  32.76  30.70
## 19 Exclusion   P4    L    1  57.93  85.24
## 20   Control   P1    L    2  59.08  37.51
## 21 Exclusion   P3    L    1  61.16  39.53
## 22   Control   P2    L    2  62.35 123.78
## 23   Control   P2    L    3  90.22 113.87
## 24   Control   P1    L    3  94.54 119.22
## 25   Control   P2    L    4 114.03  76.52
## 26 Exclusion   P4    L    2 117.88 167.90
## 27 Exclusion   P3    L    2 119.84 110.27
## 28   Control   P1    L    4 121.17 116.45
## 29 Exclusion   P3    L    3 175.87 286.33
## 30 Exclusion   P4    L    3 181.09 314.49
## 31 Exclusion   P3    L    4 238.76  54.23
## 32 Exclusion   P4    L    4 242.31 304.70
## 33   Control   P2    M    1  19.95  19.73
## 34   Control   P1    M    1  20.99  20.31
## 35   Control   P1    M    2  37.54  17.62
## 36 Exclusion   P3    M    1  38.57  30.63
## 37 Exclusion   P4    M    1  39.51  45.90
## 38   Control   P2    M    2  42.83  40.52
## 39   Control   P1    M    3  61.46  98.44
## 40   Control   P2    M    3  62.46   4.81
## 41 Exclusion   P4    M    2  79.24  88.04
## 42 Exclusion   P3    M    2  81.25  83.61
## 43   Control   P2    M    4  81.78 136.66
## 44   Control   P1    M    4  82.21 160.01
## 45 Exclusion   P4    M    3 122.09  84.19
## 46 Exclusion   P3    M    3 124.08 124.09
## 47 Exclusion   P3    M    4 159.69 112.65
## 48 Exclusion   P4    M    4 161.67 256.34

dat.1 %>% head(2)

##   Treatment Plot Dose Time Resp1 Resp2
## 1   Control   P1    H    1  8.12  3.06
## 2   Control   P1    H    2 20.55 25.94

Sort by the sum of Resp1 and Resp2

dat.1 %>% arrange(Resp1+Resp2)

##    Treatment Plot Dose Time  Resp1  Resp2
## 1    Control   P1    H    1   8.12   3.06
## 2    Control   P2    H    1   8.14  23.93
## 3    Control   P2    M    1  19.95  19.73
## 4    Control   P1    M    1  20.99  20.31
## 5    Control   P2    H    2  13.36  28.02
## 6  Exclusion   P3    H    1  21.86  23.58
## 7    Control   P1    H    2  20.55  25.94
## 8  Exclusion   P4    H    1  18.82  28.60
## 9    Control   P1    L    1  31.73  21.22
## 10   Control   P1    M    2  37.54  17.62
## 11   Control   P1    H    3  27.49  29.85
## 12   Control   P2    L    1  32.76  30.70
## 13   Control   P2    M    3  62.46   4.81
## 14 Exclusion   P3    H    2  39.83  28.03
## 15 Exclusion   P3    M    1  38.57  30.63
## 16   Control   P1    H    4  44.79  25.39
## 17   Control   P2    H    3  33.37  37.17
## 18   Control   P2    H    4  39.87  38.25
## 19 Exclusion   P4    H    2  39.82  39.07
## 20 Exclusion   P3    H    3  59.53  21.32
## 21   Control   P2    M    2  42.83  40.52
## 22 Exclusion   P4    M    1  39.51  45.90
## 23   Control   P1    L    2  59.08  37.51
## 24 Exclusion   P3    L    1  61.16  39.53
## 25 Exclusion   P4    H    4  82.29  60.15
## 26 Exclusion   P4    L    1  57.93  85.24
## 27 Exclusion   P4    H    3  63.30  93.43
## 28   Control   P1    M    3  61.46  98.44
## 29 Exclusion   P3    M    2  81.25  83.61
## 30 Exclusion   P3    H    4  75.59  90.76
## 31 Exclusion   P4    M    2  79.24  88.04
## 32   Control   P2    L    2  62.35 123.78
## 33   Control   P2    L    4 114.03  76.52
## 34   Control   P2    L    3  90.22 113.87
## 35 Exclusion   P4    M    3 122.09  84.19
## 36   Control   P1    L    3  94.54 119.22
## 37   Control   P2    M    4  81.78 136.66
## 38 Exclusion   P3    L    2 119.84 110.27
## 39   Control   P1    L    4 121.17 116.45
## 40   Control   P1    M    4  82.21 160.01
## 41 Exclusion   P3    M    3 124.08 124.09
## 42 Exclusion   P3    M    4 159.69 112.65
## 43 Exclusion   P4    L    2 117.88 167.90
## 44 Exclusion   P3    L    4 238.76  54.23
## 45 Exclusion   P4    M    4 161.67 256.34
## 46 Exclusion   P3    L    3 175.87 286.33
## 47 Exclusion   P4    L    3 181.09 314.49
## 48 Exclusion   P4    L    4 242.31 304.70

dat.1 %>% head(2)

##   Treatment Plot Dose Time Resp1 Resp2
## 1   Control   P1    H    1  8.12  3.06
## 2   Control   P1    H    2 20.55 25.94

• sort by Treatment and then Time

dat.1 %>% head(2)

##   Treatment Plot Dose Time Resp1 Resp2
## 1   Control   P1    H    1  8.12  3.06
## 2   Control   P1    H    2 20.55 25.94

• sort by Treatment and then Time

dat.1 %>% arrange(Treatment,Time)

##    Treatment Plot Dose Time  Resp1  Resp2
## 1    Control   P1    H    1   8.12   3.06
## 2    Control   P1    M    1  20.99  20.31
## 3    Control   P1    L    1  31.73  21.22
## 4    Control   P2    H    1   8.14  23.93
## 5    Control   P2    M    1  19.95  19.73
## 6    Control   P2    L    1  32.76  30.70
## 7    Control   P1    H    2  20.55  25.94
## 8    Control   P1    M    2  37.54  17.62
## 9    Control   P1    L    2  59.08  37.51
## 10   Control   P2    H    2  13.36  28.02
## 11   Control   P2    M    2  42.83  40.52
## 12   Control   P2    L    2  62.35 123.78
## 13   Control   P1    H    3  27.49  29.85
## 14   Control   P1    M    3  61.46  98.44
## 15   Control   P1    L    3  94.54 119.22
## 16   Control   P2    H    3  33.37  37.17
## 17   Control   P2    M    3  62.46   4.81
## 18   Control   P2    L    3  90.22 113.87
## 19   Control   P1    H    4  44.79  25.39
## 20   Control   P1    M    4  82.21 160.01
## 21   Control   P1    L    4 121.17 116.45
## 22   Control   P2    H    4  39.87  38.25
## 23   Control   P2    M    4  81.78 136.66
## 24   Control   P2    L    4 114.03  76.52
## 25 Exclusion   P3    H    1  21.86  23.58
## 26 Exclusion   P3    M    1  38.57  30.63
## 27 Exclusion   P3    L    1  61.16  39.53
## 28 Exclusion   P4    H    1  18.82  28.60
## 29 Exclusion   P4    M    1  39.51  45.90
## 30 Exclusion   P4    L    1  57.93  85.24
## 31 Exclusion   P3    H    2  39.83  28.03
## 32 Exclusion   P3    M    2  81.25  83.61
## 33 Exclusion   P3    L    2 119.84 110.27
## 34 Exclusion   P4    H    2  39.82  39.07
## 35 Exclusion   P4    M    2  79.24  88.04
## 36 Exclusion   P4    L    2 117.88 167.90
## 37 Exclusion   P3    H    3  59.53  21.32
## 38 Exclusion   P3    M    3 124.08 124.09
## 39 Exclusion   P3    L    3 175.87 286.33
## 40 Exclusion   P4    H    3  63.30  93.43
## 41 Exclusion   P4    M    3 122.09  84.19
## 42 Exclusion   P4    L    3 181.09 314.49
## 43 Exclusion   P3    H    4  75.59  90.76
## 44 Exclusion   P3    M    4 159.69 112.65
## 45 Exclusion   P3    L    4 238.76  54.23
## 46 Exclusion   P4    H    4  82.29  60.15
## 47 Exclusion   P4    M    4 161.67 256.34
## 48 Exclusion   P4    L    4 242.31 304.70

dat.1 %>% head(2)

##   Treatment Plot Dose Time Resp1 Resp2
## 1   Control   P1    H    1  8.12  3.06
## 2   Control   P1    H    2 20.55 25.94

• sort by Treatment and then the mean of Resp 1 and Resp2

dat.1 %>% head(2)

##   Treatment Plot Dose Time Resp1 Resp2
## 1   Control   P1    H    1  8.12  3.06
## 2   Control   P1    H    2 20.55 25.94

• sort by Treatment and then the mean of Resp1 and Resp2

dat.1 %>% arrange(Treatment, mean(c(Resp1, Resp2)))

##    Treatment Plot Dose Time  Resp1  Resp2
## 1    Control   P1    H    1   8.12   3.06
## 2    Control   P1    H    2  20.55  25.94
## 3    Control   P1    H    3  27.49  29.85
## 4    Control   P1    H    4  44.79  25.39
## 5    Control   P1    M    1  20.99  20.31
## 6    Control   P1    M    2  37.54  17.62
## 7    Control   P1    M    3  61.46  98.44
## 8    Control   P1    M    4  82.21 160.01
## 9    Control   P1    L    1  31.73  21.22
## 10   Control   P1    L    2  59.08  37.51
## 11   Control   P1    L    3  94.54 119.22
## 12   Control   P1    L    4 121.17 116.45
## 13   Control   P2    H    1   8.14  23.93
## 14   Control   P2    H    2  13.36  28.02
## 15   Control   P2    H    3  33.37  37.17
## 16   Control   P2    H    4  39.87  38.25
## 17   Control   P2    M    1  19.95  19.73
## 18   Control   P2    M    2  42.83  40.52
## 19   Control   P2    M    3  62.46   4.81
## 20   Control   P2    M    4  81.78 136.66
## 21   Control   P2    L    1  32.76  30.70
## 22   Control   P2    L    2  62.35 123.78
## 23   Control   P2    L    3  90.22 113.87
## 24   Control   P2    L    4 114.03  76.52
## 25 Exclusion   P3    H    1  21.86  23.58
## 26 Exclusion   P3    H    2  39.83  28.03
## 27 Exclusion   P3    H    3  59.53  21.32
## 28 Exclusion   P3    H    4  75.59  90.76
## 29 Exclusion   P3    M    1  38.57  30.63
## 30 Exclusion   P3    M    2  81.25  83.61
## 31 Exclusion   P3    M    3 124.08 124.09
## 32 Exclusion   P3    M    4 159.69 112.65
## 33 Exclusion   P3    L    1  61.16  39.53
## 34 Exclusion   P3    L    2 119.84 110.27
## 35 Exclusion   P3    L    3 175.87 286.33
## 36 Exclusion   P3    L    4 238.76  54.23
## 37 Exclusion   P4    H    1  18.82  28.60
## 38 Exclusion   P4    H    2  39.82  39.07
## 39 Exclusion   P4    H    3  63.30  93.43
## 40 Exclusion   P4    H    4  82.29  60.15
## 41 Exclusion   P4    M    1  39.51  45.90
## 42 Exclusion   P4    M    2  79.24  88.04
## 43 Exclusion   P4    M    3 122.09  84.19
## 44 Exclusion   P4    M    4 161.67 256.34
## 45 Exclusion   P4    L    1  57.93  85.24
## 46 Exclusion   P4    L    2 117.88 167.90
## 47 Exclusion   P4    L    3 181.09 314.49
## 48 Exclusion   P4    L    4 242.31 304.70

dat.1 %>% head(2)

##   Treatment Plot Dose Time Resp1 Resp2
## 1   Control   P1    H    1  8.12  3.06
## 2   Control   P1    H    2 20.55 25.94

dat.1 %>% mutate(Sum = Resp1 + Resp2)

##    Treatment Plot Dose Time  Resp1  Resp2    Sum
## 1    Control   P1    H    1   8.12   3.06  11.18
## 2    Control   P1    H    2  20.55  25.94  46.49
## 3    Control   P1    H    3  27.49  29.85  57.34
## 4    Control   P1    H    4  44.79  25.39  70.18
## 5    Control   P1    M    1  20.99  20.31  41.30
## 6    Control   P1    M    2  37.54  17.62  55.16
## 7    Control   P1    M    3  61.46  98.44 159.90
## 8    Control   P1    M    4  82.21 160.01 242.22
## 9    Control   P1    L    1  31.73  21.22  52.95
## 10   Control   P1    L    2  59.08  37.51  96.59
## 11   Control   P1    L    3  94.54 119.22 213.76
## 12   Control   P1    L    4 121.17 116.45 237.62
## 13   Control   P2    H    1   8.14  23.93  32.07
## 14   Control   P2    H    2  13.36  28.02  41.38
## 15   Control   P2    H    3  33.37  37.17  70.54
## 16   Control   P2    H    4  39.87  38.25  78.12
## 17   Control   P2    M    1  19.95  19.73  39.68
## 18   Control   P2    M    2  42.83  40.52  83.35
## 19   Control   P2    M    3  62.46   4.81  67.27
## 20   Control   P2    M    4  81.78 136.66 218.44
## 21   Control   P2    L    1  32.76  30.70  63.46
## 22   Control   P2    L    2  62.35 123.78 186.13
## 23   Control   P2    L    3  90.22 113.87 204.09
## 24   Control   P2    L    4 114.03  76.52 190.55
## 25 Exclusion   P3    H    1  21.86  23.58  45.44
## 26 Exclusion   P3    H    2  39.83  28.03  67.86
## 27 Exclusion   P3    H    3  59.53  21.32  80.85
## 28 Exclusion   P3    H    4  75.59  90.76 166.35
## 29 Exclusion   P3    M    1  38.57  30.63  69.20
## 30 Exclusion   P3    M    2  81.25  83.61 164.86
## 31 Exclusion   P3    M    3 124.08 124.09 248.17
## 32 Exclusion   P3    M    4 159.69 112.65 272.34
## 33 Exclusion   P3    L    1  61.16  39.53 100.69
## 34 Exclusion   P3    L    2 119.84 110.27 230.11
## 35 Exclusion   P3    L    3 175.87 286.33 462.20
## 36 Exclusion   P3    L    4 238.76  54.23 292.99
## 37 Exclusion   P4    H    1  18.82  28.60  47.42
## 38 Exclusion   P4    H    2  39.82  39.07  78.89
## 39 Exclusion   P4    H    3  63.30  93.43 156.73
## 40 Exclusion   P4    H    4  82.29  60.15 142.44
## 41 Exclusion   P4    M    1  39.51  45.90  85.41
## 42 Exclusion   P4    M    2  79.24  88.04 167.28
## 43 Exclusion   P4    M    3 122.09  84.19 206.28
## 44 Exclusion   P4    M    4 161.67 256.34 418.01
## 45 Exclusion   P4    L    1  57.93  85.24 143.17
## 46 Exclusion   P4    L    2 117.88 167.90 285.78
## 47 Exclusion   P4    L    3 181.09 314.49 495.58
## 48 Exclusion   P4    L    4 242.31 304.70 547.01

dat.1 %>% head(2)

##   Treatment Plot Dose Time Resp1 Resp2
## 1   Control   P1    H    1  8.12  3.06
## 2   Control   P1    H    2 20.55 25.94

Transformations

dat.1 %>% mutate(logResp1=log(Resp1))

##    Treatment Plot Dose Time  Resp1  Resp2 logResp1
## 1    Control   P1    H    1   8.12   3.06 2.094330
## 2    Control   P1    H    2  20.55  25.94 3.022861
## 3    Control   P1    H    3  27.49  29.85 3.313822
## 4    Control   P1    H    4  44.79  25.39 3.801985
## 5    Control   P1    M    1  20.99  20.31 3.044046
## 6    Control   P1    M    2  37.54  17.62 3.625407
## 7    Control   P1    M    3  61.46  98.44 4.118387
## 8    Control   P1    M    4  82.21 160.01 4.409277
## 9    Control   P1    L    1  31.73  21.22 3.457263
## 10   Control   P1    L    2  59.08  37.51 4.078892
## 11   Control   P1    L    3  94.54 119.22 4.549023
## 12   Control   P1    L    4 121.17 116.45 4.797195
## 13   Control   P2    H    1   8.14  23.93 2.096790
## 14   Control   P2    H    2  13.36  28.02 2.592265
## 15   Control   P2    H    3  33.37  37.17 3.507657
## 16   Control   P2    H    4  39.87  38.25 3.685624
## 17   Control   P2    M    1  19.95  19.73 2.993229
## 18   Control   P2    M    2  42.83  40.52 3.757239
## 19   Control   P2    M    3  62.46   4.81 4.134526
## 20   Control   P2    M    4  81.78 136.66 4.404033
## 21   Control   P2    L    1  32.76  30.70 3.489208
## 22   Control   P2    L    2  62.35 123.78 4.132764
## 23   Control   P2    L    3  90.22 113.87 4.502251
## 24   Control   P2    L    4 114.03  76.52 4.736462
## 25 Exclusion   P3    H    1  21.86  23.58 3.084658
## 26 Exclusion   P3    H    2  39.83  28.03 3.684620
## 27 Exclusion   P3    H    3  59.53  21.32 4.086480
## 28 Exclusion   P3    H    4  75.59  90.76 4.325324
## 29 Exclusion   P3    M    1  38.57  30.63 3.652475
## 30 Exclusion   P3    M    2  81.25  83.61 4.397531
## 31 Exclusion   P3    M    3 124.08 124.09 4.820927
## 32 Exclusion   P3    M    4 159.69 112.65 5.073234
## 33 Exclusion   P3    L    1  61.16  39.53 4.113493
## 34 Exclusion   P3    L    2 119.84 110.27 4.786158
## 35 Exclusion   P3    L    3 175.87 286.33 5.169745
## 36 Exclusion   P3    L    4 238.76  54.23 5.475459
## 37 Exclusion   P4    H    1  18.82  28.60 2.934920
## 38 Exclusion   P4    H    2  39.82  39.07 3.684369
## 39 Exclusion   P4    H    3  63.30  93.43 4.147885
## 40 Exclusion   P4    H    4  82.29  60.15 4.410250
## 41 Exclusion   P4    M    1  39.51  45.90 3.676554
## 42 Exclusion   P4    M    2  79.24  88.04 4.372481
## 43 Exclusion   P4    M    3 122.09  84.19 4.804758
## 44 Exclusion   P4    M    4 161.67 256.34 5.085557
## 45 Exclusion   P4    L    1  57.93  85.24 4.059235
## 46 Exclusion   P4    L    2 117.88 167.90 4.769667
## 47 Exclusion   P4    L    3 181.09 314.49 5.198994
## 48 Exclusion   P4    L    4 242.31 304.70 5.490218

dat.1 %>% head(2) %>% as_tibble

## # A tibble: 2 x 6
##   Treatment Plot  Dose   Time Resp1 Resp2
##   <fct>     <fct> <fct> <int> <dbl> <dbl>
## 1 Control   P1    H         1  8.12  3.06
## 2 Control   P1    H         2 20.6  25.9

Changing vector types (classes)

dat.1 %>% mutate(Time=factor(Time)) %>% as_tibble

## # A tibble: 48 x 6
##    Treatment Plot  Dose  Time  Resp1  Resp2
##    <fct>     <fct> <fct> <fct> <dbl>  <dbl>
##  1 Control   P1    H     1      8.12   3.06
##  2 Control   P1    H     2     20.6   25.9 
##  3 Control   P1    H     3     27.5   29.8 
##  4 Control   P1    H     4     44.8   25.4 
##  5 Control   P1    M     1     21.0   20.3 
##  6 Control   P1    M     2     37.5   17.6 
##  7 Control   P1    M     3     61.5   98.4 
##  8 Control   P1    M     4     82.2  160.  
##  9 Control   P1    L     1     31.7   21.2 
## 10 Control   P1    L     2     59.1   37.5 
## # … with 38 more rows

dat.1 %>% head(2) %>% as_tibble

## # A tibble: 2 x 6
##   Treatment Plot  Dose   Time Resp1 Resp2
##   <fct>     <fct> <fct> <int> <dbl> <dbl>
## 1 Control   P1    H         1  8.12  3.06
## 2 Control   P1    H         2 20.6  25.9

Changing factor labels

dat.1 %>% mutate(Dose=fct_recode(Dose, High='H',  Medium='M' )) %>%
  as_tibble

## # A tibble: 48 x 6
##    Treatment Plot  Dose    Time Resp1  Resp2
##    <fct>     <fct> <fct>  <int> <dbl>  <dbl>
##  1 Control   P1    High       1  8.12   3.06
##  2 Control   P1    High       2 20.6   25.9 
##  3 Control   P1    High       3 27.5   29.8 
##  4 Control   P1    High       4 44.8   25.4 
##  5 Control   P1    Medium     1 21.0   20.3 
##  6 Control   P1    Medium     2 37.5   17.6 
##  7 Control   P1    Medium     3 61.5   98.4 
##  8 Control   P1    Medium     4 82.2  160.  
##  9 Control   P1    L          1 31.7   21.2 
## 10 Control   P1    L          2 59.1   37.5 
## # … with 38 more rows

dat.1 %>% head(2) %>% as_tibble

## # A tibble: 2 x 6
##   Treatment Plot  Dose   Time Resp1 Resp2
##   <fct>     <fct> <fct> <int> <dbl> <dbl>
## 1 Control   P1    H         1  8.12  3.06
## 2 Control   P1    H         2 20.6  25.9

Changing factor levels

dat.1 %>% pull(Dose)

##  [1] H H H H M M M M L L L L H H H H M M M M L L L L H H H H M M M M L L L L H H H H M M M M L L L L
## Levels: H L M

dat.1 %>% mutate(Dose=fct_relevel(Dose, c('L', 'M','H'))) %>%
                    as_tibble() %>% pull(Dose)

##  [1] H H H H M M M M L L L L H H H H M M M M L L L L H H H H M M M M L L L L H H H H M M M M L L L L
## Levels: L M H

data.1 %>% head(2) %>% as_tibble

## # A tibble: 2 x 7
##   Between Plot  Cond   Time  Temp   LAT  LONG
##   <fct>   <fct> <fct> <int> <dbl> <dbl> <dbl>
## 1 A1      P1    H         1  26.3  14.0  150.
## 2 A1      P1    M         2  27.9  12.9  145.

Changing factor levels

dat.1 %>% pull(Dose)

##  [1] H H H H M M M M L L L L H H H H M M M M L L L L H H H H M M M M L L L L H H H H M M M M L L L L
## Levels: H L M

dat.1 %>% mutate(Dose=recode_factor(Dose, 'L'='Low', 'M'='Medium')) %>%
                    as_tibble() %>% pull(Dose)

##  [1] H      H      H      H      Medium Medium Medium Medium Low    Low    Low    Low    H     
## [14] H      H      H      Medium Medium Medium Medium Low    Low    Low    Low    H      H     
## [27] H      H      Medium Medium Medium Medium Low    Low    Low    Low    H      H      H     
## [40] H      Medium Medium Medium Medium Low    Low    Low    Low   
## Levels: Low Medium H

dat.1 %>% head(2)

##   Treatment Plot Dose Time Resp1 Resp2
## 1   Control   P1    H    1  8.12  3.06
## 2   Control   P1    H    2 20.55 25.94

Bin Time into Start (1 and 2) and end (3 and 4)

dat.1 %>% head(2)

##   Treatment Plot Dose Time Resp1 Resp2
## 1   Control   P1    H    1  8.12  3.06
## 2   Control   P1    H    2 20.55 25.94

Bin Time into Start (1 and 2) and end (3 and 4)

Assuming even spread..

dat.1 %>% mutate(Period = cut(Time, breaks=2, labels=c('Start', 'End'))) 

##    Treatment Plot Dose Time  Resp1  Resp2 Period
## 1    Control   P1    H    1   8.12   3.06  Start
## 2    Control   P1    H    2  20.55  25.94  Start
## 3    Control   P1    H    3  27.49  29.85    End
## 4    Control   P1    H    4  44.79  25.39    End
## 5    Control   P1    M    1  20.99  20.31  Start
## 6    Control   P1    M    2  37.54  17.62  Start
## 7    Control   P1    M    3  61.46  98.44    End
## 8    Control   P1    M    4  82.21 160.01    End
## 9    Control   P1    L    1  31.73  21.22  Start
## 10   Control   P1    L    2  59.08  37.51  Start
## 11   Control   P1    L    3  94.54 119.22    End
## 12   Control   P1    L    4 121.17 116.45    End
## 13   Control   P2    H    1   8.14  23.93  Start
## 14   Control   P2    H    2  13.36  28.02  Start
## 15   Control   P2    H    3  33.37  37.17    End
## 16   Control   P2    H    4  39.87  38.25    End
## 17   Control   P2    M    1  19.95  19.73  Start
## 18   Control   P2    M    2  42.83  40.52  Start
## 19   Control   P2    M    3  62.46   4.81    End
## 20   Control   P2    M    4  81.78 136.66    End
## 21   Control   P2    L    1  32.76  30.70  Start
## 22   Control   P2    L    2  62.35 123.78  Start
## 23   Control   P2    L    3  90.22 113.87    End
## 24   Control   P2    L    4 114.03  76.52    End
## 25 Exclusion   P3    H    1  21.86  23.58  Start
## 26 Exclusion   P3    H    2  39.83  28.03  Start
## 27 Exclusion   P3    H    3  59.53  21.32    End
## 28 Exclusion   P3    H    4  75.59  90.76    End
## 29 Exclusion   P3    M    1  38.57  30.63  Start
## 30 Exclusion   P3    M    2  81.25  83.61  Start
## 31 Exclusion   P3    M    3 124.08 124.09    End
## 32 Exclusion   P3    M    4 159.69 112.65    End
## 33 Exclusion   P3    L    1  61.16  39.53  Start
## 34 Exclusion   P3    L    2 119.84 110.27  Start
## 35 Exclusion   P3    L    3 175.87 286.33    End
## 36 Exclusion   P3    L    4 238.76  54.23    End
## 37 Exclusion   P4    H    1  18.82  28.60  Start
## 38 Exclusion   P4    H    2  39.82  39.07  Start
## 39 Exclusion   P4    H    3  63.30  93.43    End
## 40 Exclusion   P4    H    4  82.29  60.15    End
## 41 Exclusion   P4    M    1  39.51  45.90  Start
## 42 Exclusion   P4    M    2  79.24  88.04  Start
## 43 Exclusion   P4    M    3 122.09  84.19    End
## 44 Exclusion   P4    M    4 161.67 256.34    End
## 45 Exclusion   P4    L    1  57.93  85.24  Start
## 46 Exclusion   P4    L    2 117.88 167.90  Start
## 47 Exclusion   P4    L    3 181.09 314.49    End
## 48 Exclusion   P4    L    4 242.31 304.70    End

dat.1 %>% head(2)

##   Treatment Plot Dose Time Resp1 Resp2
## 1   Control   P1    H    1  8.12  3.06
## 2   Control   P1    H    2 20.55 25.94

dat.1 %>% summarise(MeanResp1=mean(Resp1), VarResp1=var(Resp1), N=n())

##   MeanResp1 VarResp1  N
## 1  75.26604 3200.305 48

SE <- function(x) sd(x)/sqrt(length(x))
dat.1 %>% summarise(MeanResp1=mean(Resp1), VarResp1=var(Resp1), 
          SEM=SE(Resp1))

##   MeanResp1 VarResp1      SEM
## 1  75.26604 3200.305 8.165355

Across versions

dat.1 %>% summarise(across(c(Resp1, Resp2), list(Mean=mean, Var=var)))

##   Resp1_Mean Resp1_Var Resp2_Mean Resp2_Var
## 1   75.26604  3200.305   81.70958  5886.444

dat.1 %>% summarise(across(where(is.numeric), list(Mean=mean, Var=var)))

##   Time_Mean Time_Var Resp1_Mean Resp1_Var Resp2_Mean Resp2_Var
## 1       2.5 1.276596   75.26604  3200.305   81.70958  5886.444

dat.1 %>% summarize( across(where(is.numeric),  mean),
          across(where(is.factor),  length))

##   Time    Resp1    Resp2 Treatment Plot Dose
## 1  2.5 75.26604 81.70958        48   48   48

Across versions

Var=c('Resp1', 'Resp2')
dat.1 %>% summarise(across(all_of(Var), list(Mean=mean, Var=var)))

##   Resp1_Mean Resp1_Var Resp2_Mean Resp2_Var
## 1   75.26604  3200.305   81.70958  5886.444

dat.1 %>% count(Dose)

##   Dose  n
## 1    H 16
## 2    L 16
## 3    M 16

dat.1 %>% count(Dose,between(Resp1,30,50))

##   Dose between(Resp1, 30, 50)  n
## 1    H                  FALSE 11
## 2    H                   TRUE  5
## 3    L                  FALSE 14
## 4    L                   TRUE  2
## 5    M                  FALSE 12
## 6    M                   TRUE  4

dat.1 %>% head(6)

##   Treatment Plot Dose Time Resp1 Resp2
## 1   Control   P1    H    1  8.12  3.06
## 2   Control   P1    H    2 20.55 25.94
## 3   Control   P1    H    3 27.49 29.85
## 4   Control   P1    H    4 44.79 25.39
## 5   Control   P1    M    1 20.99 20.31
## 6   Control   P1    M    2 37.54 17.62

dat.1 %>% group_by(Treatment,Plot) %>%
    summarise(Mean=mean(Resp1))

## # A tibble: 4 x 3
## # Groups:   Treatment [2]
##   Treatment Plot   Mean
##   <fct>     <fct> <dbl>
## 1 Control   P1     50.8
## 2 Control   P2     50.1
## 3 Exclusion P3     99.7
## 4 Exclusion P4    100.

dat.1 %>% head(6)

##   Treatment Plot Dose Time Resp1 Resp2
## 1   Control   P1    H    1  8.12  3.06
## 2   Control   P1    H    2 20.55 25.94
## 3   Control   P1    H    3 27.49 29.85
## 4   Control   P1    H    4 44.79 25.39
## 5   Control   P1    M    1 20.99 20.31
## 6   Control   P1    M    2 37.54 17.62

dat.1 %>% group_by(Treatment,Plot) %>%
    summarise(Mean=mean(Resp1), Var=var(Resp1), N=n(),First=first(Resp1))

## # A tibble: 4 x 6
## # Groups:   Treatment [2]
##   Treatment Plot   Mean   Var     N First
##   <fct>     <fct> <dbl> <dbl> <int> <dbl>
## 1 Control   P1     50.8 1162.    12  8.12
## 2 Control   P2     50.1 1069.    12  8.14
## 3 Exclusion P3     99.7 4285.    12 21.9 
## 4 Exclusion P4    100.  4470.    12 18.8

mutate vs summarise

dat.1 %>% group_by(Treatment,Plot) %>%
    summarise(Mean=mean(Resp1))

## # A tibble: 4 x 3
## # Groups:   Treatment [2]
##   Treatment Plot   Mean
##   <fct>     <fct> <dbl>
## 1 Control   P1     50.8
## 2 Control   P2     50.1
## 3 Exclusion P3     99.7
## 4 Exclusion P4    100.

dat.1 %>% group_by(Treatment,Plot) %>%
    mutate(Mean=mean(Resp1))

## # A tibble: 48 x 7
## # Groups:   Treatment, Plot [4]
##    Treatment Plot  Dose   Time Resp1  Resp2  Mean
##    <fct>     <fct> <fct> <int> <dbl>  <dbl> <dbl>
##  1 Control   P1    H         1  8.12   3.06  50.8
##  2 Control   P1    H         2 20.6   25.9   50.8
##  3 Control   P1    H         3 27.5   29.8   50.8
##  4 Control   P1    H         4 44.8   25.4   50.8
##  5 Control   P1    M         1 21.0   20.3   50.8
##  6 Control   P1    M         2 37.5   17.6   50.8
##  7 Control   P1    M         3 61.5   98.4   50.8
##  8 Control   P1    M         4 82.2  160.    50.8
##  9 Control   P1    L         1 31.7   21.2   50.8
## 10 Control   P1    L         2 59.1   37.5   50.8
## # … with 38 more rows

dat.1 %>% head(2)

##   Treatment Plot Dose Time Resp1 Resp2
## 1   Control   P1    H    1  8.12  3.06
## 2   Control   P1    H    2 20.55 25.94

dat.1 %>% group_by(Treatment,Plot) %>%
  summarise(across(everything(), mean))

## # A tibble: 4 x 6
## # Groups:   Treatment [2]
##   Treatment Plot   Dose  Time Resp1 Resp2
##   <fct>     <fct> <dbl> <dbl> <dbl> <dbl>
## 1 Control   P1       NA   2.5  50.8  56.3
## 2 Control   P2       NA   2.5  50.1  56.2
## 3 Exclusion P3       NA   2.5  99.7  83.8
## 4 Exclusion P4       NA   2.5 100.  131.

dat.1 %>% head(2)

##   Treatment Plot Dose Time Resp1 Resp2
## 1   Control   P1    H    1  8.12  3.06
## 2   Control   P1    H    2 20.55 25.94

dat.1 %>% select(-Dose,-Time) %>% group_by(Treatment,Plot) %>%
    summarise_all(list(mean))

## # A tibble: 4 x 4
## # Groups:   Treatment [2]
##   Treatment Plot  Resp1 Resp2
##   <fct>     <fct> <dbl> <dbl>
## 1 Control   P1     50.8  56.3
## 2 Control   P2     50.1  56.2
## 3 Exclusion P3     99.7  83.8
## 4 Exclusion P4    100.  131.

dat.1 %>% head(2)

##   Treatment Plot Dose Time Resp1 Resp2
## 1   Control   P1    H    1  8.12  3.06
## 2   Control   P1    H    2 20.55 25.94

dat.1 %>% group_by(Treatment,Plot) %>%
    summarise(across(c(Time,Resp1,Resp2), list(Mean=mean, SE=SE)))

## # A tibble: 4 x 8
## # Groups:   Treatment [2]
##   Treatment Plot  Time_Mean Time_SE Resp1_Mean Resp1_SE Resp2_Mean
##   <fct>     <fct>     <dbl>   <dbl>      <dbl>    <dbl>      <dbl>
## 1 Control   P1          2.5   0.337       50.8     9.84       56.3
## 2 Control   P2          2.5   0.337       50.1     9.44       56.2
## 3 Exclusion P3          2.5   0.337       99.7    18.9        83.8
## 4 Exclusion P4          2.5   0.337      100.     19.3       131. 
## # … with 1 more variable: Resp2_SE <dbl>

Calculate for each year, the mean abundance of Pocillopora damicornis

tikus %>% group_by(time) %>%
    summarise(MeanAbundance=mean(`Pocillopora damicornis`))

## # A tibble: 6 x 2
##   time  MeanAbundance
## * <fct>         <dbl>
## 1 81             30  
## 2 83              0  
## 3 84              0  
## 4 85              0  
## 5 87              1.8
## 6 88              4

Calculate for each year, the number of samples as well as the mean and variance of ozone

nasa = as.data.frame(nasa)
head(nasa)

##        lat   long month year cloudhigh cloudlow cloudmid ozone pressure surftemp temperature
## 1 36.20000 -113.8     1 1995      26.0      7.5     34.5   304      835    272.7       272.1
## 2 33.70435 -113.8     1 1995      20.0     11.5     32.5   304      940    279.5       282.2
## 3 31.20870 -113.8     1 1995      16.0     16.5     26.0   298      960    284.7       285.2
## 4 28.71304 -113.8     1 1995      13.0     20.5     14.5   276      990    289.3       290.7
## 5 26.21739 -113.8     1 1995       7.5     26.0     10.5   274     1000    292.2       292.7
## 6 23.72174 -113.8     1 1995       8.0     30.0      9.5   264     1000    294.1       293.6

Calculate for each year, the number of samples as well as the mean and variance of ozone

nasa %>% group_by(year) %>%
    summarise(N=n(),Mean=mean(ozone), Var=var(ozone))

## # A tibble: 6 x 4
##    year     N  Mean   Var
## * <int> <int> <dbl> <dbl>
## 1  1995  6912  264.  258.
## 2  1996  6912  267.  326.
## 3  1997  6912  266.  327.
## 4  1998  6912  267.  507.
## 5  1999  6912  270.  368.
## 6  2000  6912  269.  353.

dat.1 %>% head(2)

##   Treatment Plot Dose Time Resp1 Resp2
## 1   Control   P1    H    1  8.12  3.06
## 2   Control   P1    H    2 20.55 25.94

dat.1 %>% select(Treatment,Plot,Dose,Time,Resp1)

##    Treatment Plot Dose Time  Resp1
## 1    Control   P1    H    1   8.12
## 2    Control   P1    H    2  20.55
## 3    Control   P1    H    3  27.49
## 4    Control   P1    H    4  44.79
## 5    Control   P1    M    1  20.99
## 6    Control   P1    M    2  37.54
## 7    Control   P1    M    3  61.46
## 8    Control   P1    M    4  82.21
## 9    Control   P1    L    1  31.73
## 10   Control   P1    L    2  59.08
## 11   Control   P1    L    3  94.54
## 12   Control   P1    L    4 121.17
## 13   Control   P2    H    1   8.14
## 14   Control   P2    H    2  13.36
## 15   Control   P2    H    3  33.37
## 16   Control   P2    H    4  39.87
## 17   Control   P2    M    1  19.95
## 18   Control   P2    M    2  42.83
## 19   Control   P2    M    3  62.46
## 20   Control   P2    M    4  81.78
## 21   Control   P2    L    1  32.76
## 22   Control   P2    L    2  62.35
## 23   Control   P2    L    3  90.22
## 24   Control   P2    L    4 114.03
## 25 Exclusion   P3    H    1  21.86
## 26 Exclusion   P3    H    2  39.83
## 27 Exclusion   P3    H    3  59.53
## 28 Exclusion   P3    H    4  75.59
## 29 Exclusion   P3    M    1  38.57
## 30 Exclusion   P3    M    2  81.25
## 31 Exclusion   P3    M    3 124.08
## 32 Exclusion   P3    M    4 159.69
## 33 Exclusion   P3    L    1  61.16
## 34 Exclusion   P3    L    2 119.84
## 35 Exclusion   P3    L    3 175.87
## 36 Exclusion   P3    L    4 238.76
## 37 Exclusion   P4    H    1  18.82
## 38 Exclusion   P4    H    2  39.82
## 39 Exclusion   P4    H    3  63.30
## 40 Exclusion   P4    H    4  82.29
## 41 Exclusion   P4    M    1  39.51
## 42 Exclusion   P4    M    2  79.24
## 43 Exclusion   P4    M    3 122.09
## 44 Exclusion   P4    M    4 161.67
## 45 Exclusion   P4    L    1  57.93
## 46 Exclusion   P4    L    2 117.88
## 47 Exclusion   P4    L    3 181.09
## 48 Exclusion   P4    L    4 242.31

dat.1 %>% head(2)

##   Treatment Plot Dose Time Resp1 Resp2
## 1   Control   P1    H    1  8.12  3.06
## 2   Control   P1    H    2 20.55 25.94

dat.1 %>% select(-Resp2)

##    Treatment Plot Dose Time  Resp1
## 1    Control   P1    H    1   8.12
## 2    Control   P1    H    2  20.55
## 3    Control   P1    H    3  27.49
## 4    Control   P1    H    4  44.79
## 5    Control   P1    M    1  20.99
## 6    Control   P1    M    2  37.54
## 7    Control   P1    M    3  61.46
## 8    Control   P1    M    4  82.21
## 9    Control   P1    L    1  31.73
## 10   Control   P1    L    2  59.08
## 11   Control   P1    L    3  94.54
## 12   Control   P1    L    4 121.17
## 13   Control   P2    H    1   8.14
## 14   Control   P2    H    2  13.36
## 15   Control   P2    H    3  33.37
## 16   Control   P2    H    4  39.87
## 17   Control   P2    M    1  19.95
## 18   Control   P2    M    2  42.83
## 19   Control   P2    M    3  62.46
## 20   Control   P2    M    4  81.78
## 21   Control   P2    L    1  32.76
## 22   Control   P2    L    2  62.35
## 23   Control   P2    L    3  90.22
## 24   Control   P2    L    4 114.03
## 25 Exclusion   P3    H    1  21.86
## 26 Exclusion   P3    H    2  39.83
## 27 Exclusion   P3    H    3  59.53
## 28 Exclusion   P3    H    4  75.59
## 29 Exclusion   P3    M    1  38.57
## 30 Exclusion   P3    M    2  81.25
## 31 Exclusion   P3    M    3 124.08
## 32 Exclusion   P3    M    4 159.69
## 33 Exclusion   P3    L    1  61.16
## 34 Exclusion   P3    L    2 119.84
## 35 Exclusion   P3    L    3 175.87
## 36 Exclusion   P3    L    4 238.76
## 37 Exclusion   P4    H    1  18.82
## 38 Exclusion   P4    H    2  39.82
## 39 Exclusion   P4    H    3  63.30
## 40 Exclusion   P4    H    4  82.29
## 41 Exclusion   P4    M    1  39.51
## 42 Exclusion   P4    M    2  79.24
## 43 Exclusion   P4    M    3 122.09
## 44 Exclusion   P4    M    4 161.67
## 45 Exclusion   P4    L    1  57.93
## 46 Exclusion   P4    L    2 117.88
## 47 Exclusion   P4    L    3 181.09
## 48 Exclusion   P4    L    4 242.31

helper functions

• contains()
• ends_with()
• starts_with()
• matches()
• everything()
• across()
• …

must evaluate to TRUE/FALSE

helper functions

dat.1 %>% head(2)

##   Treatment Plot Dose Time Resp1 Resp2
## 1   Control   P1    H    1  8.12  3.06
## 2   Control   P1    H    2 20.55 25.94

dat.1 %>% select(contains('R'))

##    Treatment  Resp1  Resp2
## 1    Control   8.12   3.06
## 2    Control  20.55  25.94
## 3    Control  27.49  29.85
## 4    Control  44.79  25.39
## 5    Control  20.99  20.31
## 6    Control  37.54  17.62
## 7    Control  61.46  98.44
## 8    Control  82.21 160.01
## 9    Control  31.73  21.22
## 10   Control  59.08  37.51
## 11   Control  94.54 119.22
## 12   Control 121.17 116.45
## 13   Control   8.14  23.93
## 14   Control  13.36  28.02
## 15   Control  33.37  37.17
## 16   Control  39.87  38.25
## 17   Control  19.95  19.73
## 18   Control  42.83  40.52
## 19   Control  62.46   4.81
## 20   Control  81.78 136.66
## 21   Control  32.76  30.70
## 22   Control  62.35 123.78
## 23   Control  90.22 113.87
## 24   Control 114.03  76.52
## 25 Exclusion  21.86  23.58
## 26 Exclusion  39.83  28.03
## 27 Exclusion  59.53  21.32
## 28 Exclusion  75.59  90.76
## 29 Exclusion  38.57  30.63
## 30 Exclusion  81.25  83.61
## 31 Exclusion 124.08 124.09
## 32 Exclusion 159.69 112.65
## 33 Exclusion  61.16  39.53
## 34 Exclusion 119.84 110.27
## 35 Exclusion 175.87 286.33
## 36 Exclusion 238.76  54.23
## 37 Exclusion  18.82  28.60
## 38 Exclusion  39.82  39.07
## 39 Exclusion  63.30  93.43
## 40 Exclusion  82.29  60.15
## 41 Exclusion  39.51  45.90
## 42 Exclusion  79.24  88.04
## 43 Exclusion 122.09  84.19
## 44 Exclusion 161.67 256.34
## 45 Exclusion  57.93  85.24
## 46 Exclusion 117.88 167.90
## 47 Exclusion 181.09 314.49
## 48 Exclusion 242.31 304.70

helper functions

dat.1 %>% head(2)

##   Treatment Plot Dose Time Resp1 Resp2
## 1   Control   P1    H    1  8.12  3.06
## 2   Control   P1    H    2 20.55 25.94

dat.1 %>% select(starts_with('R'))

##     Resp1  Resp2
## 1    8.12   3.06
## 2   20.55  25.94
## 3   27.49  29.85
## 4   44.79  25.39
## 5   20.99  20.31
## 6   37.54  17.62
## 7   61.46  98.44
## 8   82.21 160.01
## 9   31.73  21.22
## 10  59.08  37.51
## 11  94.54 119.22
## 12 121.17 116.45
## 13   8.14  23.93
## 14  13.36  28.02
## 15  33.37  37.17
## 16  39.87  38.25
## 17  19.95  19.73
## 18  42.83  40.52
## 19  62.46   4.81
## 20  81.78 136.66
## 21  32.76  30.70
## 22  62.35 123.78
## 23  90.22 113.87
## 24 114.03  76.52
## 25  21.86  23.58
## 26  39.83  28.03
## 27  59.53  21.32
## 28  75.59  90.76
## 29  38.57  30.63
## 30  81.25  83.61
## 31 124.08 124.09
## 32 159.69 112.65
## 33  61.16  39.53
## 34 119.84 110.27
## 35 175.87 286.33
## 36 238.76  54.23
## 37  18.82  28.60
## 38  39.82  39.07
## 39  63.30  93.43
## 40  82.29  60.15
## 41  39.51  45.90
## 42  79.24  88.04
## 43 122.09  84.19
## 44 161.67 256.34
## 45  57.93  85.24
## 46 117.88 167.90
## 47 181.09 314.49
## 48 242.31 304.70

helper functions

dat.1 %>% head(2)

##   Treatment Plot Dose Time Resp1 Resp2
## 1   Control   P1    H    1  8.12  3.06
## 2   Control   P1    H    2 20.55 25.94

dat.1 %>% select(ends_with('e'))

##    Dose Time
## 1     H    1
## 2     H    2
## 3     H    3
## 4     H    4
## 5     M    1
## 6     M    2
## 7     M    3
## 8     M    4
## 9     L    1
## 10    L    2
## 11    L    3
## 12    L    4
## 13    H    1
## 14    H    2
## 15    H    3
## 16    H    4
## 17    M    1
## 18    M    2
## 19    M    3
## 20    M    4
## 21    L    1
## 22    L    2
## 23    L    3
## 24    L    4
## 25    H    1
## 26    H    2
## 27    H    3
## 28    H    4
## 29    M    1
## 30    M    2
## 31    M    3
## 32    M    4
## 33    L    1
## 34    L    2
## 35    L    3
## 36    L    4
## 37    H    1
## 38    H    2
## 39    H    3
## 40    H    4
## 41    M    1
## 42    M    2
## 43    M    3
## 44    M    4
## 45    L    1
## 46    L    2
## 47    L    3
## 48    L    4

helper functions

dat.1 %>% head(2)

##   Treatment Plot Dose Time Resp1 Resp2
## 1   Control   P1    H    1  8.12  3.06
## 2   Control   P1    H    2 20.55 25.94

dat.1 %>% select(matches('^.{4}$'))

##    Plot Dose Time
## 1    P1    H    1
## 2    P1    H    2
## 3    P1    H    3
## 4    P1    H    4
## 5    P1    M    1
## 6    P1    M    2
## 7    P1    M    3
## 8    P1    M    4
## 9    P1    L    1
## 10   P1    L    2
## 11   P1    L    3
## 12   P1    L    4
## 13   P2    H    1
## 14   P2    H    2
## 15   P2    H    3
## 16   P2    H    4
## 17   P2    M    1
## 18   P2    M    2
## 19   P2    M    3
## 20   P2    M    4
## 21   P2    L    1
## 22   P2    L    2
## 23   P2    L    3
## 24   P2    L    4
## 25   P3    H    1
## 26   P3    H    2
## 27   P3    H    3
## 28   P3    H    4
## 29   P3    M    1
## 30   P3    M    2
## 31   P3    M    3
## 32   P3    M    4
## 33   P3    L    1
## 34   P3    L    2
## 35   P3    L    3
## 36   P3    L    4
## 37   P4    H    1
## 38   P4    H    2
## 39   P4    H    3
## 40   P4    H    4
## 41   P4    M    1
## 42   P4    M    2
## 43   P4    M    3
## 44   P4    M    4
## 45   P4    L    1
## 46   P4    L    2
## 47   P4    L    3
## 48   P4    L    4

helper functions

dat.1 %>% head(2)

##   Treatment Plot Dose Time Resp1 Resp2
## 1   Control   P1    H    1  8.12  3.06
## 2   Control   P1    H    2 20.55 25.94

dat.1 %>% select(Treatment:Time)

##    Treatment Plot Dose Time
## 1    Control   P1    H    1
## 2    Control   P1    H    2
## 3    Control   P1    H    3
## 4    Control   P1    H    4
## 5    Control   P1    M    1
## 6    Control   P1    M    2
## 7    Control   P1    M    3
## 8    Control   P1    M    4
## 9    Control   P1    L    1
## 10   Control   P1    L    2
## 11   Control   P1    L    3
## 12   Control   P1    L    4
## 13   Control   P2    H    1
## 14   Control   P2    H    2
## 15   Control   P2    H    3
## 16   Control   P2    H    4
## 17   Control   P2    M    1
## 18   Control   P2    M    2
## 19   Control   P2    M    3
## 20   Control   P2    M    4
## 21   Control   P2    L    1
## 22   Control   P2    L    2
## 23   Control   P2    L    3
## 24   Control   P2    L    4
## 25 Exclusion   P3    H    1
## 26 Exclusion   P3    H    2
## 27 Exclusion   P3    H    3
## 28 Exclusion   P3    H    4
## 29 Exclusion   P3    M    1
## 30 Exclusion   P3    M    2
## 31 Exclusion   P3    M    3
## 32 Exclusion   P3    M    4
## 33 Exclusion   P3    L    1
## 34 Exclusion   P3    L    2
## 35 Exclusion   P3    L    3
## 36 Exclusion   P3    L    4
## 37 Exclusion   P4    H    1
## 38 Exclusion   P4    H    2
## 39 Exclusion   P4    H    3
## 40 Exclusion   P4    H    4
## 41 Exclusion   P4    M    1
## 42 Exclusion   P4    M    2
## 43 Exclusion   P4    M    3
## 44 Exclusion   P4    M    4
## 45 Exclusion   P4    L    1
## 46 Exclusion   P4    L    2
## 47 Exclusion   P4    L    3
## 48 Exclusion   P4    L    4

nasa %>% head()

##        lat   long month year cloudhigh cloudlow cloudmid ozone pressure surftemp temperature
## 1 36.20000 -113.8     1 1995      26.0      7.5     34.5   304      835    272.7       272.1
## 2 33.70435 -113.8     1 1995      20.0     11.5     32.5   304      940    279.5       282.2
## 3 31.20870 -113.8     1 1995      16.0     16.5     26.0   298      960    284.7       285.2
## 4 28.71304 -113.8     1 1995      13.0     20.5     14.5   276      990    289.3       290.7
## 5 26.21739 -113.8     1 1995       7.5     26.0     10.5   274     1000    292.2       292.7
## 6 23.72174 -113.8     1 1995       8.0     30.0      9.5   264     1000    294.1       293.6

Select lat, long, and cloud.. columns

nasa %>% head()

##        lat   long month year cloudhigh cloudlow cloudmid ozone pressure surftemp temperature
## 1 36.20000 -113.8     1 1995      26.0      7.5     34.5   304      835    272.7       272.1
## 2 33.70435 -113.8     1 1995      20.0     11.5     32.5   304      940    279.5       282.2
## 3 31.20870 -113.8     1 1995      16.0     16.5     26.0   298      960    284.7       285.2
## 4 28.71304 -113.8     1 1995      13.0     20.5     14.5   276      990    289.3       290.7
## 5 26.21739 -113.8     1 1995       7.5     26.0     10.5   274     1000    292.2       292.7
## 6 23.72174 -113.8     1 1995       8.0     30.0      9.5   264     1000    294.1       293.6

nasa %>% select(lat, long, starts_with("cloud")) %>% head

##        lat   long cloudhigh cloudlow cloudmid
## 1 36.20000 -113.8      26.0      7.5     34.5
## 2 33.70435 -113.8      20.0     11.5     32.5
## 3 31.20870 -113.8      16.0     16.5     26.0
## 4 28.71304 -113.8      13.0     20.5     14.5
## 5 26.21739 -113.8       7.5     26.0     10.5
## 6 23.72174 -113.8       8.0     30.0      9.5

tikus[1:10,c(1:3,76:77)]

##     Psammocora contigua Psammocora digitata Pocillopora damicornis time rep
## V1                    0                   0                     79   81   1
## V2                    0                   0                     51   81   2
## V3                    0                   0                     42   81   3
## V4                    0                   0                     15   81   4
## V5                    0                   0                      9   81   5
## V6                    0                   0                     72   81   6
## V7                    0                   0                      0   81   7
## V8                    0                   0                     16   81   8
## V9                    0                   0                      0   81   9
## V10                   0                   0                     16   81  10

Select rep, time and only Species that DONT contain pora

Select rep, time and only Species that DONT contain pora

tikas %>% dplyr::select(-contains('pora'))
## OR if we wanted to alter the order...
tikas %>% dplyr::select(rep, time, everything(),-contains('pora'))

dplyr::select(tikus, `Pocillopora damicornis`)

##     Pocillopora damicornis
## V1                      79
## V2                      51
## V3                      42
## V4                      15
## V5                       9
## V6                      72
## V7                       0
## V8                      16
## V9                       0
## V10                     16
## V11                      0
## V12                      0
## V13                      0
## V14                      0
## V15                      0
## V16                      0
## V17                      0
## V18                      0
## V19                      0
## V20                      0
## V21                      0
## V22                      0
## V23                      0
## V24                      0
## V25                      0
## V26                      0
## V27                      0
## V28                      0
## V29                      0
## V30                      0
## V31                      0
## V32                      0
## V33                      0
## V34                      0
## V35                      0
## V36                      0
## V37                      0
## V38                      0
## V39                      0
## V40                      0
## V41                     18
## V42                      0
## V43                      0
## V44                      0
## V45                      0
## V46                      0
## V47                      0
## V48                      0
## V49                      0
## V50                      0
## V51                      0
## V52                      0
## V53                      0
## V54                      0
## V55                      0
## V56                      0
## V57                     10
## V58                      0
## V59                     30
## V60                      0

dat.1 %>% pull(Resp1)

##  [1]   8.12  20.55  27.49  44.79  20.99  37.54  61.46  82.21  31.73  59.08  94.54 121.17
## [13]   8.14  13.36  33.37  39.87  19.95  42.83  62.46  81.78  32.76  62.35  90.22 114.03
## [25]  21.86  39.83  59.53  75.59  38.57  81.25 124.08 159.69  61.16 119.84 175.87 238.76
## [37]  18.82  39.82  63.30  82.29  39.51  79.24 122.09 161.67  57.93 117.88 181.09 242.31

dat.1 %>% head(2)

##   Treatment Plot Dose Time Resp1 Resp2
## 1   Control   P1    H    1  8.12  3.06
## 2   Control   P1    H    2 20.55 25.94

dat.1 %>% rename(Exposure=Treatment, Richness=Resp1)

##     Exposure Plot Dose Time Richness  Resp2
## 1    Control   P1    H    1     8.12   3.06
## 2    Control   P1    H    2    20.55  25.94
## 3    Control   P1    H    3    27.49  29.85
## 4    Control   P1    H    4    44.79  25.39
## 5    Control   P1    M    1    20.99  20.31
## 6    Control   P1    M    2    37.54  17.62
## 7    Control   P1    M    3    61.46  98.44
## 8    Control   P1    M    4    82.21 160.01
## 9    Control   P1    L    1    31.73  21.22
## 10   Control   P1    L    2    59.08  37.51
## 11   Control   P1    L    3    94.54 119.22
## 12   Control   P1    L    4   121.17 116.45
## 13   Control   P2    H    1     8.14  23.93
## 14   Control   P2    H    2    13.36  28.02
## 15   Control   P2    H    3    33.37  37.17
## 16   Control   P2    H    4    39.87  38.25
## 17   Control   P2    M    1    19.95  19.73
## 18   Control   P2    M    2    42.83  40.52
## 19   Control   P2    M    3    62.46   4.81
## 20   Control   P2    M    4    81.78 136.66
## 21   Control   P2    L    1    32.76  30.70
## 22   Control   P2    L    2    62.35 123.78
## 23   Control   P2    L    3    90.22 113.87
## 24   Control   P2    L    4   114.03  76.52
## 25 Exclusion   P3    H    1    21.86  23.58
## 26 Exclusion   P3    H    2    39.83  28.03
## 27 Exclusion   P3    H    3    59.53  21.32
## 28 Exclusion   P3    H    4    75.59  90.76
## 29 Exclusion   P3    M    1    38.57  30.63
## 30 Exclusion   P3    M    2    81.25  83.61
## 31 Exclusion   P3    M    3   124.08 124.09
## 32 Exclusion   P3    M    4   159.69 112.65
## 33 Exclusion   P3    L    1    61.16  39.53
## 34 Exclusion   P3    L    2   119.84 110.27
## 35 Exclusion   P3    L    3   175.87 286.33
## 36 Exclusion   P3    L    4   238.76  54.23
## 37 Exclusion   P4    H    1    18.82  28.60
## 38 Exclusion   P4    H    2    39.82  39.07
## 39 Exclusion   P4    H    3    63.30  93.43
## 40 Exclusion   P4    H    4    82.29  60.15
## 41 Exclusion   P4    M    1    39.51  45.90
## 42 Exclusion   P4    M    2    79.24  88.04
## 43 Exclusion   P4    M    3   122.09  84.19
## 44 Exclusion   P4    M    4   161.67 256.34
## 45 Exclusion   P4    L    1    57.93  85.24
## 46 Exclusion   P4    L    2   117.88 167.90
## 47 Exclusion   P4    L    3   181.09 314.49
## 48 Exclusion   P4    L    4   242.31 304.70

dat.1 %>% head(2)

##   Treatment Plot Dose Time Resp1 Resp2
## 1   Control   P1    H    1  8.12  3.06
## 2   Control   P1    H    2 20.55 25.94

dat.1 %>% filter(Dose=='H')

##    Treatment Plot Dose Time Resp1 Resp2
## 1    Control   P1    H    1  8.12  3.06
## 2    Control   P1    H    2 20.55 25.94
## 3    Control   P1    H    3 27.49 29.85
## 4    Control   P1    H    4 44.79 25.39
## 5    Control   P2    H    1  8.14 23.93
## 6    Control   P2    H    2 13.36 28.02
## 7    Control   P2    H    3 33.37 37.17
## 8    Control   P2    H    4 39.87 38.25
## 9  Exclusion   P3    H    1 21.86 23.58
## 10 Exclusion   P3    H    2 39.83 28.03
## 11 Exclusion   P3    H    3 59.53 21.32
## 12 Exclusion   P3    H    4 75.59 90.76
## 13 Exclusion   P4    H    1 18.82 28.60
## 14 Exclusion   P4    H    2 39.82 39.07
## 15 Exclusion   P4    H    3 63.30 93.43
## 16 Exclusion   P4    H    4 82.29 60.15

dat.1 %>% filter(Dose %in% c('H','M'))

##    Treatment Plot Dose Time  Resp1  Resp2
## 1    Control   P1    H    1   8.12   3.06
## 2    Control   P1    H    2  20.55  25.94
## 3    Control   P1    H    3  27.49  29.85
## 4    Control   P1    H    4  44.79  25.39
## 5    Control   P1    M    1  20.99  20.31
## 6    Control   P1    M    2  37.54  17.62
## 7    Control   P1    M    3  61.46  98.44
## 8    Control   P1    M    4  82.21 160.01
## 9    Control   P2    H    1   8.14  23.93
## 10   Control   P2    H    2  13.36  28.02
## 11   Control   P2    H    3  33.37  37.17
## 12   Control   P2    H    4  39.87  38.25
## 13   Control   P2    M    1  19.95  19.73
## 14   Control   P2    M    2  42.83  40.52
## 15   Control   P2    M    3  62.46   4.81
## 16   Control   P2    M    4  81.78 136.66
## 17 Exclusion   P3    H    1  21.86  23.58
## 18 Exclusion   P3    H    2  39.83  28.03
## 19 Exclusion   P3    H    3  59.53  21.32
## 20 Exclusion   P3    H    4  75.59  90.76
## 21 Exclusion   P3    M    1  38.57  30.63
## 22 Exclusion   P3    M    2  81.25  83.61
## 23 Exclusion   P3    M    3 124.08 124.09
## 24 Exclusion   P3    M    4 159.69 112.65
## 25 Exclusion   P4    H    1  18.82  28.60
## 26 Exclusion   P4    H    2  39.82  39.07
## 27 Exclusion   P4    H    3  63.30  93.43
## 28 Exclusion   P4    H    4  82.29  60.15
## 29 Exclusion   P4    M    1  39.51  45.90
## 30 Exclusion   P4    M    2  79.24  88.04
## 31 Exclusion   P4    M    3 122.09  84.19
## 32 Exclusion   P4    M    4 161.67 256.34

dat.1 %>% head(2)

##   Treatment Plot Dose Time Resp1 Resp2
## 1   Control   P1    H    1  8.12  3.06
## 2   Control   P1    H    2 20.55 25.94

dat.1 %>% filter(Dose=='H' & Resp1<25)

##   Treatment Plot Dose Time Resp1 Resp2
## 1   Control   P1    H    1  8.12  3.06
## 2   Control   P1    H    2 20.55 25.94
## 3   Control   P2    H    1  8.14 23.93
## 4   Control   P2    H    2 13.36 28.02
## 5 Exclusion   P3    H    1 21.86 23.58
## 6 Exclusion   P4    H    1 18.82 28.60

dat.1 %>% filter(Dose=='H' | Resp1<25)

##    Treatment Plot Dose Time Resp1 Resp2
## 1    Control   P1    H    1  8.12  3.06
## 2    Control   P1    H    2 20.55 25.94
## 3    Control   P1    H    3 27.49 29.85
## 4    Control   P1    H    4 44.79 25.39
## 5    Control   P1    M    1 20.99 20.31
## 6    Control   P2    H    1  8.14 23.93
## 7    Control   P2    H    2 13.36 28.02
## 8    Control   P2    H    3 33.37 37.17
## 9    Control   P2    H    4 39.87 38.25
## 10   Control   P2    M    1 19.95 19.73
## 11 Exclusion   P3    H    1 21.86 23.58
## 12 Exclusion   P3    H    2 39.83 28.03
## 13 Exclusion   P3    H    3 59.53 21.32
## 14 Exclusion   P3    H    4 75.59 90.76
## 15 Exclusion   P4    H    1 18.82 28.60
## 16 Exclusion   P4    H    2 39.82 39.07
## 17 Exclusion   P4    H    3 63.30 93.43
## 18 Exclusion   P4    H    4 82.29 60.15

dat.1 %>% head(2)

##   Treatment Plot Dose Time Resp1 Resp2
## 1   Control   P1    H    1  8.12  3.06
## 2   Control   P1    H    2 20.55 25.94

Keep only those rows with Resp1 less than 40 and Time greater than 1 or Dose is equal to L

dat.1 %>% head(2)

##   Treatment Plot Dose Time Resp1 Resp2
## 1   Control   P1    H    1  8.12  3.06
## 2   Control   P1    H    2 20.55 25.94

Keep only those rows with Resp1 less than 40 and Time greater than 1 or Dose is equal to L

dat.1 %>% filter(Resp1<40 & (Time>1 |  Dose =="L"))

##    Treatment Plot Dose Time Resp1 Resp2
## 1    Control   P1    H    2 20.55 25.94
## 2    Control   P1    H    3 27.49 29.85
## 3    Control   P1    M    2 37.54 17.62
## 4    Control   P1    L    1 31.73 21.22
## 5    Control   P2    H    2 13.36 28.02
## 6    Control   P2    H    3 33.37 37.17
## 7    Control   P2    H    4 39.87 38.25
## 8    Control   P2    L    1 32.76 30.70
## 9  Exclusion   P3    H    2 39.83 28.03
## 10 Exclusion   P4    H    2 39.82 39.07

glimpse(nasa)

## Rows: 41,472
## Columns: 11
## $ lat         <dbl> 36.200000, 33.704348, 31.208696, 28.713043, 26.217391, 23.721739, 21…
## $ long        <dbl> -113.8000, -113.8000, -113.8000, -113.8000, -113.8000, -113.8000, -1…
## $ month       <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ year        <int> 1995, 1995, 1995, 1995, 1995, 1995, 1995, 1995, 1995, 1995, 1995, 19…
## $ cloudhigh   <dbl> 26.0, 20.0, 16.0, 13.0, 7.5, 8.0, 14.5, 19.5, 22.5, 21.0, 19.0, 16.5…
## $ cloudlow    <dbl> 7.5, 11.5, 16.5, 20.5, 26.0, 30.0, 29.5, 26.5, 27.5, 26.0, 28.5, 28.…
## $ cloudmid    <dbl> 34.5, 32.5, 26.0, 14.5, 10.5, 9.5, 11.0, 17.5, 18.5, 16.5, 12.5, 13.…
## $ ozone       <dbl> 304, 304, 298, 276, 274, 264, 258, 252, 250, 250, 248, 248, 250, 248…
## $ pressure    <dbl> 835, 940, 960, 990, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, …
## $ surftemp    <dbl> 272.7, 279.5, 284.7, 289.3, 292.2, 294.1, 295.0, 298.3, 300.1, 300.1…
## $ temperature <dbl> 272.1, 282.2, 285.2, 290.7, 292.7, 293.6, 294.6, 296.9, 297.8, 298.7…

Filter to the largest ozone value for the second month of the last year

Filter to the largest ozone value for the second month of the last year

nasa %>% filter(year==max(year) & month==2) %>% 
    arrange(-ozone) %>% head(5)
nasa %>% filter(year==max(year) & month==2) %>%
    arrange(-ozone) %>% slice(1:5)
##OR
nasa %>% filter(year==max(year) & month==2 ) %>%
    top_n(5, ozone)

Filter to all ozone values between 320 and 325 in the first month of the last year

glimpse(nasa)

## Rows: 41,472
## Columns: 11
## $ lat         <dbl> 36.200000, 33.704348, 31.208696, 28.713043, 26.217391, 23.721739, 21…
## $ long        <dbl> -113.8000, -113.8000, -113.8000, -113.8000, -113.8000, -113.8000, -1…
## $ month       <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ year        <int> 1995, 1995, 1995, 1995, 1995, 1995, 1995, 1995, 1995, 1995, 1995, 19…
## $ cloudhigh   <dbl> 26.0, 20.0, 16.0, 13.0, 7.5, 8.0, 14.5, 19.5, 22.5, 21.0, 19.0, 16.5…
## $ cloudlow    <dbl> 7.5, 11.5, 16.5, 20.5, 26.0, 30.0, 29.5, 26.5, 27.5, 26.0, 28.5, 28.…
## $ cloudmid    <dbl> 34.5, 32.5, 26.0, 14.5, 10.5, 9.5, 11.0, 17.5, 18.5, 16.5, 12.5, 13.…
## $ ozone       <dbl> 304, 304, 298, 276, 274, 264, 258, 252, 250, 250, 248, 248, 250, 248…
## $ pressure    <dbl> 835, 940, 960, 990, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, …
## $ surftemp    <dbl> 272.7, 279.5, 284.7, 289.3, 292.2, 294.1, 295.0, 298.3, 300.1, 300.1…
## $ temperature <dbl> 272.1, 282.2, 285.2, 290.7, 292.7, 293.6, 294.6, 296.9, 297.8, 298.7…

Filter to all ozone values between 320 and 325 in the first month of the last year

nasa %>% filter(ozone > 320 & ozone<325, month==first(month),
       year==last(year))
##OR
nasa %>% filter(between(ozone,320,325), month==first(month),
       year==last(year))

Filtering by row number

dat.1 %>% head(2)

##   Treatment Plot Dose Time Resp1 Resp2
## 1   Control   P1    H    1  8.12  3.06
## 2   Control   P1    H    2 20.55 25.94

dat.1 %>% slice(1:4)

##   Treatment Plot Dose Time Resp1 Resp2
## 1   Control   P1    H    1  8.12  3.06
## 2   Control   P1    H    2 20.55 25.94
## 3   Control   P1    H    3 27.49 29.85
## 4   Control   P1    H    4 44.79 25.39

data.1 %>% slice(c(1:4,7))

## # A tibble: 5 x 7
##   Between Plot  Cond   Time  Temp   LAT  LONG
##   <fct>   <fct> <fct> <int> <dbl> <dbl> <dbl>
## 1 A1      P1    H         1  26.3  14.0  150.
## 2 A1      P1    M         2  27.9  12.9  145.
## 3 A1      P1    L         3  28.3  12.3  146.
## 4 A1      P2    H         4  28.4  12.1  145.
## 5 A2      P3    H         3  25.8  14.6  147.

dat.1 %>% head(2)

##   Treatment Plot Dose Time Resp1 Resp2
## 1   Control   P1    H    1  8.12  3.06
## 2   Control   P1    H    2 20.55 25.94

dat.1 %>% sample_n(10, replace=TRUE)

##    Treatment Plot Dose Time  Resp1  Resp2
## 1    Control   P1    H    1   8.12   3.06
## 2  Exclusion   P4    H    4  82.29  60.15
## 3    Control   P1    M    2  37.54  17.62
## 4    Control   P2    L    3  90.22 113.87
## 5  Exclusion   P4    M    4 161.67 256.34
## 6    Control   P1    M    2  37.54  17.62
## 7  Exclusion   P4    H    4  82.29  60.15
## 8  Exclusion   P4    H    3  63.30  93.43
## 9    Control   P1    L    3  94.54 119.22
## 10   Control   P2    M    1  19.95  19.73

dat.1 %>% head(2)

##   Treatment Plot Dose Time Resp1 Resp2
## 1   Control   P1    H    1  8.12  3.06
## 2   Control   P1    H    2 20.55 25.94

dat.1 %>% sample_frac(0.5, replace=TRUE)

##    Treatment Plot Dose Time  Resp1  Resp2
## 1  Exclusion   P3    L    4 238.76  54.23
## 2    Control   P2    H    1   8.14  23.93
## 3  Exclusion   P4    H    4  82.29  60.15
## 4  Exclusion   P3    H    1  21.86  23.58
## 5  Exclusion   P4    L    4 242.31 304.70
## 6  Exclusion   P3    H    1  21.86  23.58
## 7    Control   P2    L    3  90.22 113.87
## 8  Exclusion   P4    L    2 117.88 167.90
## 9    Control   P2    M    4  81.78 136.66
## 10 Exclusion   P4    H    3  63.30  93.43
## 11 Exclusion   P3    M    1  38.57  30.63
## 12   Control   P2    H    1   8.14  23.93
## 13   Control   P2    L    2  62.35 123.78
## 14 Exclusion   P3    M    1  38.57  30.63
## 15 Exclusion   P3    H    4  75.59  90.76
## 16 Exclusion   P4    M    4 161.67 256.34
## 17 Exclusion   P4    L    4 242.31 304.70
## 18 Exclusion   P3    L    1  61.16  39.53
## 19 Exclusion   P4    L    1  57.93  85.24
## 20   Control   P2    L    1  32.76  30.70
## 21 Exclusion   P3    M    3 124.08 124.09
## 22 Exclusion   P4    H    2  39.82  39.07
## 23   Control   P2    M    1  19.95  19.73
## 24   Control   P1    L    1  31.73  21.22

dat.1 %>% head(2)

##   Treatment Plot Dose Time Resp1 Resp2
## 1   Control   P1    H    1  8.12  3.06
## 2   Control   P1    H    2 20.55 25.94

#examine the levels of the Cond factor
levels(dat.1$Dose)

## [1] "H" "L" "M"

#subset the dataset to just Cond H
dat.3 <- dat.1 %>% filter(Plot=='P1')
#examine subset data
dat.3

##    Treatment Plot Dose Time  Resp1  Resp2
## 1    Control   P1    H    1   8.12   3.06
## 2    Control   P1    H    2  20.55  25.94
## 3    Control   P1    H    3  27.49  29.85
## 4    Control   P1    H    4  44.79  25.39
## 5    Control   P1    M    1  20.99  20.31
## 6    Control   P1    M    2  37.54  17.62
## 7    Control   P1    M    3  61.46  98.44
## 8    Control   P1    M    4  82.21 160.01
## 9    Control   P1    L    1  31.73  21.22
## 10   Control   P1    L    2  59.08  37.51
## 11   Control   P1    L    3  94.54 119.22
## 12   Control   P1    L    4 121.17 116.45

#examine the levels of the Cond factor
levels(dat.3$Dose)

## [1] "H" "L" "M"

levels(dat.3$Plot)

## [1] "P1" "P2" "P3" "P4"

levels(dat.3$Treatment)

## [1] "Control"   "Exclusion"

Correction - all factors

#subset the dataset to just Dose H
dat.3 <-  dat.1 %>% filter(Plot=='P1')
#drop the unused factor levels from all factors
dat.3 <- dat.3 %>% droplevels()
#examine the levels of each factor
levels(dat.3$Dose)

## [1] "H" "L" "M"

levels(dat.3$Plot)

## [1] "P1"

levels(dat.3$Treatment)

## [1] "Control"

Correction - single factor

#subset the dataset to just Dose H
dat.3 <- dat.1 %>% filter(Plot=='P1')
#drop the unused factor levels from Dose
dat.3 <- dat.3 %>% mutate(Plot=factor(Plot))
#examine the levels of each factor
levels(dat.3$Dose)

## [1] "H" "L" "M"

levels(dat.3$Plot)

## [1] "P1"

levels(dat.3$Treatment)

## [1] "Control"   "Exclusion"

Wide data

Wide to long (melt)

data.w %>% pivot_longer(Time.0:Time.2,  names_to = 'Time',
                        values_to='Count')
## OR
data.w %>% pivot_longer(c(-Between, -Plot),  names_to = 'Time',
                        values_to='Count')

## # A tibble: 12 x 4
##    Between Plot  Time   Count
##    <fct>   <fct> <chr>  <int>
##  1 A1      P1    Time.0     8
##  2 A1      P1    Time.1    14
##  3 A1      P1    Time.2    14
##  4 A1      P2    Time.0    10
##  5 A1      P2    Time.1    12
##  6 A1      P2    Time.2    11
##  7 A2      P3    Time.0     7
##  8 A2      P3    Time.1    11
##  9 A2      P3    Time.2     8
## 10 A2      P4    Time.0    11
## 11 A2      P4    Time.1     9
## 12 A2      P4    Time.2     2
## # A tibble: 12 x 4
##    Between Plot  Time   Count
##    <fct>   <fct> <chr>  <int>
##  1 A1      P1    Time.0     8
##  2 A1      P1    Time.1    14
##  3 A1      P1    Time.2    14
##  4 A1      P2    Time.0    10
##  5 A1      P2    Time.1    12
##  6 A1      P2    Time.2    11
##  7 A2      P3    Time.0     7
##  8 A2      P3    Time.1    11
##  9 A2      P3    Time.2     8
## 10 A2      P4    Time.0    11
## 11 A2      P4    Time.1     9
## 12 A2      P4    Time.2     2

Wide data

Wide to long (melt)

## OR
data.w %>% pivot_longer(starts_with('Time'),  names_to = 'Time',
                        values_to='Count',
                        names_prefix='Time.')

## # A tibble: 12 x 4
##    Between Plot  Time  Count
##    <fct>   <fct> <chr> <int>
##  1 A1      P1    0         8
##  2 A1      P1    1        14
##  3 A1      P1    2        14
##  4 A1      P2    0        10
##  5 A1      P2    1        12
##  6 A1      P2    2        11
##  7 A2      P3    0         7
##  8 A2      P3    1        11
##  9 A2      P3    2         8
## 10 A2      P4    0        11
## 11 A2      P4    1         9
## 12 A2      P4    2         2

Long data

data %>% head(2)

##   Resp1 Resp2 Between Plot Subplot Within
## 1     8    17      A1   P1      S1     B1
## 2    10    18      A1   P1      S1     B2

Widen (cast)

Widen Resp1 for repeated measures (Within)

data %>% select(-Resp2) %>%
  pivot_wider(names_from=Within,  values_from=c(Resp1))

## # A tibble: 12 x 5
##    Between Plot  Subplot    B1    B2
##    <fct>   <fct> <fct>   <int> <int>
##  1 A1      P1    S1          8    10
##  2 A1      P1    S2          7    11
##  3 A2      P2    S3         14    12
##  4 A2      P2    S4         11     9
##  5 A3      P3    S5         14    11
##  6 A3      P3    S6          8     2
##  7 A1      P4    S7          8    10
##  8 A1      P4    S8          7    12
##  9 A2      P5    S9         11    12
## 10 A2      P5    S10        12    10
## 11 A3      P6    S11         3    11
## 12 A3      P6    S12        13     7

Widen Resp1 and Resp2 for repeated measures (Within)

data %>% head(2)

##   Resp1 Resp2 Between Plot Subplot Within
## 1     8    17      A1   P1      S1     B1
## 2    10    18      A1   P1      S1     B2

data %>% pivot_wider(names_from=Within, values_from=c(Resp1, Resp2))

## # A tibble: 12 x 7
##    Between Plot  Subplot Resp1_B1 Resp1_B2 Resp2_B1 Resp2_B2
##    <fct>   <fct> <fct>      <int>    <int>    <int>    <int>
##  1 A1      P1    S1             8       10       17       18
##  2 A1      P1    S2             7       11       17       21
##  3 A2      P2    S3            14       12       19       13
##  4 A2      P2    S4            11        9       24       18
##  5 A3      P3    S5            14       11       25       18
##  6 A3      P3    S6             8        2       27       22
##  7 A1      P4    S7             8       10       17       22
##  8 A1      P4    S8             7       12       16       13
##  9 A2      P5    S9            11       12       23       19
## 10 A2      P5    S10           12       10       23       21
## 11 A3      P6    S11            3       11       17       16
## 12 A3      P6    S12           13        7       26       28

Bio data (missing Subplot 3)

Physio-chemical data (missing S7)

Merge bio and chem data (only keep full matches - an inner join)

data.bio %>% inner_join(data.chem)

##    Resp1 Resp2 Between Plot Subplot     Chem1     Chem2
## 1      8    18      A1   P1      S1  1.452878 0.8858208
## 2     10    21      A1   P1      S2  3.266253 0.1800177
## 3     11    23      A1   P2      S4 13.400350 1.5762780
## 4     14    22      A2   P3      S5  3.779183 1.6222430
## 5     12    24      A2   P3      S6  1.196657 4.2369184
## 6      9    20      A2   P4      S8  5.687807 2.9859003
## 7     14    11      A3   P5      S9  4.834518 4.1328919
## 8     11    22      A3   P5     S10  2.002931 3.6043314
## 9      8    24      A3   P6     S11 12.326867 1.7763576
## 10     2    16      A3   P6     S12  4.014221 0.2255188

• S3 and S7 absent

Merge bio and chem data (keep all data - outer join)

data.bio %>% full_join(data.chem)

##    Resp1 Resp2 Between Plot Subplot     Chem1     Chem2
## 1      8    18      A1   P1      S1  1.452878 0.8858208
## 2     10    21      A1   P1      S2  3.266253 0.1800177
## 3     11    23      A1   P2      S4 13.400350 1.5762780
## 4     14    22      A2   P3      S5  3.779183 1.6222430
## 5     12    24      A2   P3      S6  1.196657 4.2369184
## 6     11    23      A2   P4      S7        NA        NA
## 7      9    20      A2   P4      S8  5.687807 2.9859003
## 8     14    11      A3   P5      S9  4.834518 4.1328919
## 9     11    22      A3   P5     S10  2.002931 3.6043314
## 10     8    24      A3   P6     S11 12.326867 1.7763576
## 11     2    16      A3   P6     S12  4.014221 0.2255188
## 12    NA    NA      A1   P2      S3  1.178652 5.0780682

• note the order of Subplot

Merge bio and chem data (only keep full BIO matches - left join)

data.bio %>% left_join(data.chem)

##    Resp1 Resp2 Between Plot Subplot     Chem1     Chem2
## 1      8    18      A1   P1      S1  1.452878 0.8858208
## 2     10    21      A1   P1      S2  3.266253 0.1800177
## 3     11    23      A1   P2      S4 13.400350 1.5762780
## 4     14    22      A2   P3      S5  3.779183 1.6222430
## 5     12    24      A2   P3      S6  1.196657 4.2369184
## 6     11    23      A2   P4      S7        NA        NA
## 7      9    20      A2   P4      S8  5.687807 2.9859003
## 8     14    11      A3   P5      S9  4.834518 4.1328919
## 9     11    22      A3   P5     S10  2.002931 3.6043314
## 10     8    24      A3   P6     S11 12.326867 1.7763576
## 11     2    16      A3   P6     S12  4.014221 0.2255188

Merge bio and chem data (only keep full CHEM matches - right join)

data.bio %>% right_join(data.chem)

##    Resp1 Resp2 Between Plot Subplot     Chem1     Chem2
## 1      8    18      A1   P1      S1  1.452878 0.8858208
## 2     10    21      A1   P1      S2  3.266253 0.1800177
## 3     11    23      A1   P2      S4 13.400350 1.5762780
## 4     14    22      A2   P3      S5  3.779183 1.6222430
## 5     12    24      A2   P3      S6  1.196657 4.2369184
## 6      9    20      A2   P4      S8  5.687807 2.9859003
## 7     14    11      A3   P5      S9  4.834518 4.1328919
## 8     11    22      A3   P5     S10  2.002931 3.6043314
## 9      8    24      A3   P6     S11 12.326867 1.7763576
## 10     2    16      A3   P6     S12  4.014221 0.2255188
## 11    NA    NA      A1   P2      S3  1.178652 5.0780682

Biological data set (data.bio)

##    Resp1 Resp2 Between Plot Subplot
## 1      8    18      A1   P1      S1
## 2     10    21      A1   P1      S2
## 4     11    23      A1   P2      S4
## 5     14    22      A2   P3      S5
## 6     12    24      A2   P3      S6
## 7     11    23      A2   P4      S7
## 8      9    20      A2   P4      S8
## 9     14    11      A3   P5      S9
## 10    11    22      A3   P5     S10
## 11     8    24      A3   P6     S11
## 12     2    16      A3   P6     S12

Geographical data set (lookup table) (data.geo)

##   Plot     LAT     LONG
## 1   P1 17.9605 145.4326
## 2   P2 17.5210 146.1983
## 3   P3 17.0011 146.3839
## 4   P4 18.2350 146.7934
## 5   P5 18.9840 146.0345
## 6   P6 20.1154 146.4672

Incorporate (merge) the lat/longs into the bio data

data.bio %>% left_join(data.geo,by=c("Plot"))

##    Resp1 Resp2 Between Plot Subplot     LAT     LONG
## 1      8    18      A1   P1      S1 17.9605 145.4326
## 2     10    21      A1   P1      S2 17.9605 145.4326
## 3     11    23      A1   P2      S4 17.5210 146.1983
## 4     14    22      A2   P3      S5 17.0011 146.3839
## 5     12    24      A2   P3      S6 17.0011 146.3839
## 6     11    23      A2   P4      S7 18.2350 146.7934
## 7      9    20      A2   P4      S8 18.2350 146.7934
## 8     14    11      A3   P5      S9 18.9840 146.0345
## 9     11    22      A3   P5     S10 18.9840 146.0345
## 10     8    24      A3   P6     S11 20.1154 146.4672
## 11     2    16      A3   P6     S12 20.1154 146.4672

##     Psammocora contigua Psammocora digitata time rep
## V1                    0                   0   81   1
## V2                    0                   0   81   2
## V3                    0                   0   81   3
## V4                    0                   0   81   4
## V5                    0                   0   81   5
## V6                    0                   0   81   6
## V7                    0                   0   81   7
## V8                    0                   0   81   8
## V9                    0                   0   81   9
## V10                   0                   0   81  10

## Rows: 60
## Columns: 77
## $ `Psammocora contigua`    <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `Psammocora digitata`    <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `Pocillopora damicornis` <int> 79, 51, 42, 15, 9, 72, 0, 16, 0, 16…
## $ `Pocillopora verrucosa`  <int> 32, 21, 35, 0, 0, 0, 41, 25, 38, 0,…
## $ `Stylopora pistillata`   <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `Acropora bruegemanni`   <int> 0, 44, 0, 11, 9, 10, 0, 0, 0, 37, 0…
## $ `Acropora robusta`       <int> 0, 35, 40, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `Acropora grandis`       <int> 0, 0, 0, 0, 0, 0, 60, 0, 0, 0, 0, 0…
## $ `Acropora intermedia`    <int> 30, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ `Acropora formosa`       <int> 75, 0, 15, 0, 125, 0, 0, 0, 10, 0, …
## $ `Acropora splendida`     <int> 0, 22, 0, 31, 0, 9, 16, 0, 0, 20, 0…
## $ `Acropera aspera`        <int> 17, 18, 9, 8, 23, 0, 17, 13, 16, 18…
## $ `Acropora hyacinthus`    <int> 141, 34, 55, 54, 0, 0, 0, 0, 0, 0, …
## $ `Acropora palifera`      <int> 32, 0, 44, 0, 17, 0, 0, 0, 0, 0, 0,…
## $ `Acropora cytherea`      <int> 108, 33, 14, 122, 0, 0, 0, 8, 0, 0,…
## $ `Acropora tenuis`        <int> 0, 25, 0, 0, 0, 22, 28, 0, 0, 0, 0,…
## $ `Acropora pulchra`       <int> 0, 0, 15, 52, 62, 33, 0, 0, 24, 0, …
## $ `Acropora nasuta`        <int> 43, 21, 19, 0, 0, 0, 10, 0, 0, 0, 0…
## $ `Acropora humilis`       <int> 31, 25, 0, 19, 0, 0, 0, 0, 0, 0, 0,…
## $ `Acropora diversa`       <int> 22, 19, 20, 13, 23, 14, 0, 12, 12, …
## $ `Acropora digitifera`    <int> 30, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ `Acropora divaricata`    <int> 0, 32, 55, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `Acropora subglabra`     <int> 51, 0, 0, 44, 15, 0, 0, 25, 0, 0, 0…
## $ `Acropora cerealis`      <int> 0, 75, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ `Acropora valida`        <int> 0, 0, 0, 30, 0, 0, 0, 0, 0, 0, 0, 0…
## $ `Acropora acuminata`     <int> 20, 0, 71, 0, 15, 0, 25, 25, 0, 0, …
## $ `Acropora elsevi`        <int> 30, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ `Acropora millepora`     <int> 17, 14, 0, 20, 0, 0, 0, 0, 0, 0, 0,…
## $ `Montipora monasteriata` <int> 60, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ `Montipora tuberculosa`  <int> 0, 15, 15, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `Montipora hispida`      <int> 0, 0, 0, 32, 40, 24, 0, 0, 0, 0, 0,…
## $ `Montipora digitata`     <int> 0, 0, 0, 0, 0, 77, 84, 53, 71, 351,…
## $ `Montipora foliosa`      <int> 0, 0, 0, 0, 50, 71, 62, 81, 24, 0, …
## $ `Montipora verrucosa`    <int> 0, 0, 30, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ `Fungia fungites`        <int> 0, 0, 18, 17, 0, 0, 0, 0, 0, 0, 0, …
## $ `Fungia paumotensis`     <int> 0, 33, 0, 0, 0, 0, 0, 0, 0, 0, 12, …
## $ `Fungia concina`         <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10…
## $ `Fungia scutaria`        <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `Halomitra limax`        <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `Pavona varians`         <int> 30, 0, 0, 0, 0, 0, 0, 0, 0, 0, 30, …
## $ `Pavona venosa`          <int> 0, 24, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ `Pavona cactus`          <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `Coeloseris mayeri`      <int> 20, 0, 15, 0, 9, 19, 0, 0, 25, 0, 0…
## $ `Galaxea fascicularis`   <int> 51, 27, 31, 24, 0, 13, 0, 0, 0, 0, …
## $ `Symphyllia radians`     <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `Lobophyllia corymbosa`  <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 12…
## $ `Lobophyllia hemprichii` <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `Porites cylindrica`     <int> 61, 24, 0, 20, 0, 0, 0, 0, 0, 0, 10…
## $ `Porites lichen`         <int> 0, 47, 49, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `Porites lobata`         <int> 36, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ `Porites lutea`          <int> 30, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ `Porites nigrescens`     <int> 0, 0, 0, 21, 0, 9, 25, 0, 45, 26, 0…
## $ `Porites solida`         <int> 0, 0, 10, 0, 17, 0, 31, 41, 0, 0, 0…
## $ `Porites stephensoni`    <int> 0, 0, 0, 0, 0, 0, 0, 30, 0, 0, 0, 0…
## $ `Goniopora lobata`       <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `Favia pallida`          <int> 10, 20, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `Favia speciosa`         <int> 0, 0, 30, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ `Favia stelligera`       <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `Favia rotumana`         <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `Favites abdita`         <int> 33, 41, 23, 27, 91, 63, 72, 48, 71,…
## $ `Favites chinensis`      <int> 0, 44, 78, 61, 44, 0, 55, 30, 30, 0…
## $ `Goniastrea rectiformis` <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0,…
## $ `Goniastrea pectinata`   <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `Goniastrea sp`          <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `Dulophyllia crispa`     <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9,…
## $ `Platygyra daedalea`     <int> 0, 27, 55, 0, 71, 74, 55, 48, 0, 0,…
## $ `Platygyra sinensis`     <int> 47, 27, 56, 26, 0, 0, 0, 0, 0, 0, 0…
## $ `Hydnopora rigida`       <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `Leptastrea purpurea`    <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `Leptastrea pruinosa`    <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `Cyphastrea serailia`    <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 19, 0…
## $ `Millepora platyphylla`  <int> 30, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ `Millepora dichotoma`    <int> 21, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ `Millepora intrincata`   <int> 24, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ `Heliopora coerulea`     <int> 461, 271, 221, 154, 0, 0, 0, 0, 0, …
## $ time                     <fct> 81, 81, 81, 81, 81, 81, 81, 81, 81,…
## $ rep                      <fct> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1, 2…

Explore/Process data

• Convert abundance to cover
• Mean cover of total Acropora per year
• NOTE there is a typo ‘Acropera’

Explore/Process data

• Convert abundance to cover (abundance is the length in cm of a 10m transect containing the species)
• Mean cover of total Acropora per year
• NOTE there is a typo ‘Acropera’

Step 1. fix typo (rename) - backticks

tikus %>% rename(`Acropora aspera`=`Acropera aspera`)

Explore/Process data

• Convert abundance to cover
• Mean cover of total Acropora per year
• NOTE there is a typo ‘Acropera’

Step 2. melt data (gather)

tikus %>% rename(`Acropora aspera`=`Acropera aspera`) %>%
    gather(Species, Abundance,-time,-rep)

Explore/Process data

• Convert abundance to cover
• Mean cover of total Acropora per year
• NOTE there is a typo ‘Acropera’

Step 3. Calculate Cover (mutate) (Abundance/10)

tikus %>% rename(`Acropora aspera`=`Acropera aspera`) %>%
    gather(Species, Abundance,-time,-rep) %>%
    mutate(Cover=Abundance/10)

Explore/Process data

• Convert abundance to cover
• Mean cover of total Acropora per year
• NOTE there is a typo ‘Acropera’

Step 4. Split species into Genera and Species (separate)

tikus %>% rename(`Acropora aspera`=`Acropera aspera`) %>%
    gather(Species, Abundance,-time,-rep) %>%
    mutate(Cover=Abundance/10) %>%
    separate(Species,c('Genera','Species'))

Explore/Process data

• Convert abundance to cover
• Mean cover of total Acropora per year
• NOTE there is a typo ‘Acropera’

Step 5. Subset just ‘Acropora’ (filter)

tikus %>% rename(`Acropora aspera`=`Acropera aspera`) %>%
    gather(Species, Abundance,-time,-rep) %>%
    mutate(Cover=Abundance/10) %>%
    separate(Species,c('Genera','Species')) %>%
    filter(Genera=='Acropora')

Explore/Process data

• Convert abundance to cover
• Mean cover of total Acropora per year
• NOTE there is a typo ‘Acropera’

Step 6. Sum over all Species (group_by and summarise)

tikus %>% rename(`Acropora aspera`=`Acropera aspera`) %>%
    gather(Species, Abundance,-time,-rep) %>%
    mutate(Cover=Abundance/10) %>%
    separate(Species,c('Genera','Species')) %>%
    filter(Genera=='Acropora') %>%
    group_by(time,rep) %>%
    summarise(SumCover=sum(Cover))

Explore/Process data

• Convert abundance to cover
• Mean cover of total Acropora per year
• NOTE there is a typo ‘Acropera’

Step 7. Summarise per year

tikus %>% rename(`Acropora aspera`=`Acropera aspera`) %>%
    gather(Species, Abundance,-time,-rep) %>%
    mutate(Cover=Abundance/10) %>%
    separate(Species,c('Genera','Species')) %>%
    filter(Genera=='Acropora') %>%
    group_by(time,rep) %>%
    summarise(SumCover=sum(Cover)) %>%
    group_by(time) %>%
    summarise(Mean=mean(SumCover),
              Var=var(SumCover))

## # A tibble: 6 x 3
##   time   Mean   Var
## * <fct> <dbl> <dbl>
## 1 81    25.6  383. 
## 2 83     0      0  
## 3 84     0      0  
## 4 85     2.43  14.2
## 5 87     8.01  68.5
## 6 88     8.55 106.

tikus %>% rename(`Acropora aspera`=`Acropera aspera`) %>%
    gather(Species, Abundance,-time,-rep) %>%
    mutate(Cover=Abundance/10) %>%
    separate(Species,c('Genera','Species')) %>%
    filter(Genera=='Acropora') %>%
    group_by(time,rep) %>%
    summarise(SumCover=sum(Cover)) %>%
    group_by(time) %>%
    summarise(Mean=mean(SumCover),
              Var=var(SumCover))

## # A tibble: 6 x 3
##   time   Mean   Var
## * <fct> <dbl> <dbl>
## 1 81    25.6  383. 
## 2 83     0      0  
## 3 84     0      0  
## 4 85     2.43  14.2
## 5 87     8.01  68.5
## 6 88     8.55 106.

Can you modify so that we get the means and var for each Genera per year?

tikus %>% rename(`Acropora aspera`=`Acropera aspera`) %>%
    gather(Species, Abundance,-time,-rep) %>%
    mutate(Cover=Abundance/10) %>%
    separate(Species,c('Genera','Species')) %>%
    group_by(time,rep,Genera) %>%
    summarise(SumCover=sum(Cover)) %>%
    group_by(time,Genera) %>%
    summarise(Mean=mean(SumCover),
              Var=var(SumCover))

## # A tibble: 144 x 4
## # Groups:   time [6]
##    time  Genera       Mean    Var
##    <fct> <chr>       <dbl>  <dbl>
##  1 81    Acropora    25.6  383.  
##  2 81    Coeloseris   0.88   1.02
##  3 81    Cyphastrea   0      0   
##  4 81    Dulophyllia  0      0   
##  5 81    Favia        0.6    1.16
##  6 81    Favites      8.22  14.9 
##  7 81    Fungia       0.68   1.38
##  8 81    Galaxea      1.46   3.23
##  9 81    Goniastrea   0      0   
## 10 81    Goniopora    0      0   
## # … with 134 more rows

What about the means and var for the top 3 Genera per year (sorted from highest to lowest)?

tikus %>% rename(`Acropora aspera`=`Acropera aspera`) %>%
    gather(Species, Abundance,-time,-rep) %>%
    mutate(Cover=Abundance/10) %>%
    separate(Species,c('Genera','Species')) %>%
    group_by(time,rep,Genera) %>%
    summarise(SumCover=sum(Cover)) %>%
    group_by(time,Genera) %>%
    summarise(Mean=mean(SumCover),
              Var=var(SumCover)) %>%
    top_n(3,Mean) %>%
    arrange(desc(Mean))

## # A tibble: 18 x 4
## # Groups:   time [6]
##    time  Genera     Mean    Var
##    <fct> <chr>     <dbl>  <dbl>
##  1 87    Montipora 27.4  966.  
##  2 81    Acropora  25.6  383.  
##  3 85    Montipora 20.5  171.  
##  4 85    Porites   19.0   51.3 
##  5 88    Montipora 11.8  644.  
##  6 81    Montipora 11.4   95.7 
##  7 81    Heliopora 11.1  262.  
##  8 84    Montipora 11.0   70.5 
##  9 88    Porites    9.84  41.4 
## 10 88    Acropora   8.55 106.  
## 11 87    Acropora   8.01  68.5 
## 12 87    Porites    4.49  35.8 
## 13 84    Porites    2.94   6.65
## 14 85    Platygyra  2.55   8.74
## 15 83    Porites    1.74   2.07
## 16 84    Pavona     1.2    3.33
## 17 83    Fungia     1.14   3.64
## 18 83    Montipora  0.93   1.57